CRAN Analysis

1 Introduction

In this section, we try to answer our research questions based on the data we have collected for the R programming language. We have already gone over a general EDA, but here we want to characterize the R packages by sectors, organizations/institutions, and countries, and also attribute credit towards the most influential actors by aggregating towards these characterization variables. Also, we’d like to construct a package network to see how packages are linked to each other. Finally, we have a number of impact measures (e.g. additions, reverse dependencies…etc) we will use to identify the most important packages in the R community. A number of impact measures will only be available for the packages we were able to collect GitHub data for (e.g. stars, forks).

library(tidyverse)
library(RMySQL)
library(ggwes)
library(knitr)
library(kableExtra)
library(pander)
library(ggthemes)
library(readxl)

2 File List

2.1 Input Files

• cran: Full CRAN Database as of September 2023 with selected metadata

• cran_repos: CRAN GitHub repos loaded from database containing repository metrics

• cran_users: CRAN GitHub Users data loaded from database containing information like sector/organization

• user_commits: CRAN GitHub user commit data containing additions and deletions

• user_countries: CRAN User country data cleaned

cran <- read.csv("\\\\westat.com\\DFS\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\cran.csv")%>%
                  dplyr::select(-X)

cran_repos <- read.csv("\\\\westat.com\\DFS\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\cran_repos.csv")%>%
                    dplyr::select(-X)

cran_users <- read_excel("\\\\westat.com\\DFS\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\cran_users.xlsx")
cran_users <- cran_users[,-1]

user_commits <- read.csv("\\\\westat.com\\DFS\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\cran_user_commits.csv")%>%
                    dplyr::select(-X) 

user_countries <- read.csv("\\\\westat.com\\DFS\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\cran_user_countries.csv")%>%
                    dplyr::select(-X)

3 Analysis: R Programming Language

In this section, we try to answer our research questions based on the data we have collected for the R programming language. We have already gone over a general EDA, but here we want to characterize the R packages by sectors, organizations/institutions, and countries, and also attribute credit towards the most influential actors by aggregated towards these characterization variables. Also, we’d like to construct a package network to see how packages are linked to each other. Finally, we have a number of impact measures (e.g. additions, reverse dependencies…etc) we will use to identify the most important packages in the R community. A number of impact measures will only be available for the packages we were able to collect GitHub data for (e.g. stars, forks).

3.1 Characterizing the Open Source Software Ecosystem

3.1.1 All CRAN Packages (As of September 2023)

3.1.1.1 What is the distribution of R Packages and R Package maintainers by sector?

Out of 19,852 packages, we were not able to identify a sector for 12,721 of them. For the ones where a sector was found (7131), 6240 were identified as academic, 583 as business, 166 as government, and 142 as nonprofit

## sectors based on packages
pander(table(cran$Sector, useNA = "always"))

Academic	Business	Government	Nonprofit	Unknown	NA
6240	583	166	142	12721	0

Out of 10,821 unique maintainers, we were able to identify a sector for 4,014 of them. 3,639 are from the academic sector, 196 from the business sector, 87 from the government sector, and 92 from nonprofit sector

## sectors based on unique maintainers
cran_unique <- cran %>%
                    distinct(email, .keep_all = T)

pander(table(cran_unique$Sector, useNA = "always"))

Academic	Business	Government	Nonprofit	Unknown	NA
3639	196	87	92	6807	0

Based on all CRAN Packages that we were able to extract a sector from, 88% are academic, 8% are business, 2% are government, and 2% are nonprofit. When looking at the unique maintainers, 91% are academic, 5% are business, 2% are government, and 2% are nonprofit.

# Calculate counts by sector (All packages)
cran_sector_counts <- cran %>%
  filter(Sector != "Unknown") %>%
  count(Sector) %>%
  mutate(proportion = n / sum(n),
         proportion_label = paste0(round(proportion * 100, 1), "%")) 

# Save plot
cran_sector_counts_plot <- ggplot(cran_sector_counts, aes(x = Sector, y = n)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = proportion_label), vjust = -0.3) +
  ylab("Count of Packages") +
  ylim(c(0, 7000))+
  ggtitle(label = "Sector Distribution of All R packages")+ 
  labs(caption = "*64% Unknown for packages (removed from analysis)")+
  theme_clean()

cran_sector_counts_plot

# Calculate counts by sector (For unique Maintainers)
cran_sector_counts_unique <- cran %>%
  distinct(email, .keep_all = T)%>%
  filter(Sector != "Unknown") %>%
  count(Sector) %>%
  mutate(proportion = n / sum(n),
         proportion_label = paste0(round(proportion * 100, 1), "%")) 

# Save plot
cran_sector_counts_unique_plot <-ggplot(cran_sector_counts_unique, aes(x = Sector, y = n)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = proportion_label), vjust = -0.3) +
  ylab("Count of Maintainers") +
  ylim(c(0, 7000))+
  ggtitle(label = "Sector Distribution of Unique All R Package Maintainers")+ 
  labs(caption = "62% Unknown for unique maintainers (removed from analysis)")+
  theme_clean()

cran_sector_counts_unique_plot

3.1.1.2 What are the top 10 institutions developing R Packages based on the number of packages and number of unique maintainers?

Based on all packages, the most frequent institution identified in the maintainer email domains is Rstudio followed by Harvard University. However, if we base it on unique maintainer email domains, then Harvard becomes most frequently identified institution, followed by Rstudio. It appears that a lot of the packages developed from Rstudio domains are the same ones.

### sorting to the top 10 most common institutions for packages
top10_Institutions <- sort(table(cran$Institution), decreasing = T)
top10_Institutions <- as.data.frame(head(top10_Institutions, 10))

colnames(top10_Institutions) <- c("Institution", "Freq")

### joining to institution dataframe to get sector variable
top10_Institutions <- cran %>% 
  right_join(top10_Institutions, by = "Institution")%>%
  distinct(Institution, .keep_all = T)%>%
  select(Institution, Sector, Freq)%>%
  arrange(desc(Freq))

### sorting to the top 10 most common institutions for distinct maintainers
top10_Institutions_unique <- sort(table(cran_unique$Institution), decreasing = T)
top10_Institutions_unique <- as.data.frame(head(top10_Institutions_unique, 10))

colnames(top10_Institutions_unique) <- c("Institution", "Freq")

### joining to institution unique dataframe to get sector variable
top10_Institutions_unique <- cran %>% 
  right_join(top10_Institutions_unique, by = "Institution")%>%
  distinct(Institution, .keep_all = T)%>%
  select(Institution, Sector, Freq)%>%
  arrange(desc(Freq))

### Graph output of top 10 institutions for packages
 ggplot(top10_Institutions, aes(x = reorder(Institution, Freq), y = Freq, fill = Sector))+
   geom_bar(stat = "identity") +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of Packages",
         title = "Top 10 Institutions for All R Packages" ) +
    ylim(c(0, 350))+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme_clean()


 
 ### Graph output of top 10 institutions for unique maintainers
 ggplot(top10_Institutions_unique, aes(x = reorder(Institution, Freq), y = Freq, fill = Sector))+
   geom_bar(stat = "identity") +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of Maintainers",
         title = "Top 10 Institutions for Unique Maintainers" ) +
    ylim(c(0, 350))+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme_clean()

### Table output of top 10 Institutions for packages
top10_Institutions %>%
  kbl(caption = "Most Frequent Institutions for Packages", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Institutions for Packages

Institution	Sector	Freq
RStudio	Business	329
Harvard University	Academic	146
University of California-Berkeley	Academic	103
NetEase	Business	98
University of Washington-Seattle Campus	Academic	91
University of Michigan-Ann Arbor	Academic	91
University of Minnesota-Twin Cities	Academic	84
Stanford University	Academic	84
University of Wisconsin-Madison	Academic	79
University of Auckland	Academic	78

### Table output of top 10 Institutions for unique maintainers
top10_Institutions_unique %>%
  kbl(caption = "Most Frequent Institutions for Unique Maintainers", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Institutions for Unique Maintainers

Institution	Sector	Freq
Harvard University	Academic	71
NetEase	Business	57
University of Washington-Seattle Campus	Academic	55
University of Michigan-Ann Arbor	Academic	54
RStudio	Business	49
University of Minnesota-Twin Cities	Academic	46
University of California-Berkeley	Academic	39
Stanford University	Academic	36
University of Wisconsin-Madison	Academic	34
Google	Business	32

3.1.2 GitHub R Packages

As stated in the introduction, we also collected data from GitHub for all R packages that we were able to identify with a repository. Github provides us with more data including repository statistics and data at the contributor level, which would be each individual that is a collaborator on a given repository. We can now look at distributions at both the maintainer and contributor levels to compare. For now, we’ll still just be looking at the package level, meaning the maintainer level information of the packages.

After linking to GitHub, we are able to identify repository data for 7,844 out of the 19,852 packages on CRAN

We first have to extract the slug from all packages that have a GitHub URL

#### filtering for URLs that only contain github.com in the link
cran_github <- cran %>% filter(grepl("https://github.com", URL, ignore.case = TRUE))

### extracting the URL portion with the slug
cran_github <- cran_github %>%
          mutate(URL = str_extract(URL, "https://github.com/([^/]+)/([^/]+)"))


### removing commas
cran_github <- cran_github %>%
          mutate(URL = sub(",.*$",  "",  URL))

### extracting slug portion
cran_github <- cran_github %>%
          mutate(slug = str_extract(URL, "(?<=github.com/)[^/]+/[^/]+"))


cran_github <- cran_github %>%
          mutate(slug = str_extract(slug, "[^\\s]+/[^\\s]+"))

We can now join the original cran dataframe to the repositories we collected data for

### creating slug for linkage
cran_repos <- cran_repos %>%
  mutate(slug = paste(owner, repo, sep = "/"))

### join to cran by Package for more data
cran_repos <- cran_github %>%
                  left_join(cran_repos, by = "slug")%>%
                  distinct(slug, .keep_all = T)


### create "year_created" variable
cran_repos$year_created <- substr(cran_repos$created_at, 1, 4)

3.1.2.1 What is the distribution of GitHub R Packages and GitHub R Package maintainers by sector?

Out of 7,844 packages identified on GitHub, we were able to identify a sector for 2379 of them. For the ones where a sector was found, 1858 were identified as academic, 385 as business, 70 as government, and 66 as nonprofit

pander(table(cran_repos$Sector, useNA = "always"))

Academic	Business	Government	Nonprofit	Unknown	NA
1858	385	70	66	5465	0

Out of 4267 unique maintainers identified on GitHub, we were able to identify a sector for 1322 of them. 1132 were identified as academic, 109 as business, 39 as government, and 42 as nonprofit

## sectors based on unique maintainers
cran_repos_unique <- cran_repos %>%
                    distinct(email, .keep_all = T)

pander(table(cran_repos_unique$Sector, useNA = "always"))

Academic	Business	Government	Nonprofit	Unknown	NA
1132	109	39	42	2945	0

Based on all GitHub R Packages that we were able to extract a sector from, 78% are academic, 16% are business, 3% are government, and 3% are nonprfoit. When looking at the unique maintainers, 86% are academic, 8% are business, 3% are government, and 3% are nonprofit.

# Calculate counts by sector (All packages on GitHub)
cran_repo_sector_counts <- cran_repos %>%
  filter(Sector != "Unknown") %>%
  count(Sector) %>%
  mutate(proportion = n / sum(n),
         proportion_label = paste0(round(proportion * 100, 1), "%")) 

# Save plot
cran_repo_sector_counts_plot <- ggplot(cran_repo_sector_counts, aes(x = Sector, y = n)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = proportion_label), vjust = -0.3) +
  ylab("Count of Packages") +
  ylim(c(0, 2000))+
  ggtitle(label = "Number of R Packages on GitHub by Maintainer's Sector")+ 
  labs(caption = "*70% Unknown for packages (removed from analysis)")+
  theme_clean()

cran_repo_sector_counts_plot

# Calculate counts by sector (For unique Maintainers on GitHub)
cran_repo_sector_counts_unique <- cran_repos_unique %>%
  distinct(email, .keep_all = T)%>%
  filter(Sector != "Unknown") %>%
  count(Sector) %>%
  mutate(proportion = n / sum(n),
         proportion_label = paste0(round(proportion * 100, 1), "%")) 

# Save plot
cran_repo_sector_counts_unique_plot <-ggplot(cran_repo_sector_counts_unique, aes(x = Sector, y = n)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = proportion_label), vjust = -0.3) +
  ylab("Count of Maintainers") +
  ylim(c(0, 2000))+
  ggtitle(label = "Number of R Package Maintainers on GitHub by Sector")+
  labs(caption = "*69% Unknown for unique maintainers (removed from analysis)")+
  theme_clean()

cran_repo_sector_counts_unique_plot

3.1.2.2 What are the top 10 institutions developing R Packages on GitHub based on the number of packages and number of unique maintainers?

For number of packages overall, Rstudio develops the most R packages on Github by a good margin. However, if we look at the unique maintainers only, the spread between Rstudio and other institutions becomes much smaller. It seems that their are a few maintainers that develop a lot of the R packages. We also note that those who do not have a sector will also not have an institution label (these coincide with one another).

### sorting to the top 10 most common institutions for packages
top10_Institutions_GitHub <- sort(table(cran_repos$Institution), decreasing = T)
top10_Institutions_GitHub <- as.data.frame(head(top10_Institutions_GitHub, 10))

colnames(top10_Institutions_GitHub) <- c("Institution", "Freq")

### joining to institution dataframe to get sector variable
top10_Institutions_GitHub <- cran_repos %>% 
  right_join(top10_Institutions_GitHub, by = "Institution")%>%
  distinct(Institution, .keep_all = T)%>%
  select(Institution, Sector, Freq)%>%
  arrange(desc(Freq))

### sorting to the top 10 most common institutions for distinct maintainers
top10_Institutions_GitHub_unique <- sort(table(cran_repos_unique$Institution), decreasing = T)
top10_Institutions_GitHub_unique <- as.data.frame(head(top10_Institutions_GitHub_unique, 10))

colnames(top10_Institutions_GitHub_unique) <- c("Institution", "Freq")

### joining to institution unique dataframe to get sector variable
top10_Institutions_GitHub_unique <- cran_repos_unique %>% 
  right_join(top10_Institutions_GitHub_unique, by = "Institution")%>%
  distinct(Institution, .keep_all = T)%>%
  select(Institution, Sector, Freq)%>%
  arrange(desc(Freq))

### Graph output of top 10 institutions for packages
 ggplot(top10_Institutions_GitHub, aes(x = reorder(Institution, Freq), y = Freq, fill = Sector))+
   geom_bar(stat = "identity") +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of Packages",
         title = "Top 10 Institutions for R Packages on GitHub" ) +
    ylim(c(0, 300))+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme_clean()+
   theme(
  plot.title = element_text(size = 13))


 
 ### Graph output of top 10 institutions for unique maintainers
 ggplot(top10_Institutions_GitHub_unique, aes(x = reorder(Institution, Freq), y = Freq, fill = Sector))+
   geom_bar(stat = "identity") +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of Maintainers",
         title = "Top 10 Institutions for Unique Maintainers on GitHub" ) +
    ylim(c(0, 300))+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme_clean()+
   theme(
  plot.title = element_text(size = 13))

### Table output of top 10 Institutions for packages
top10_Institutions_GitHub %>%
  kbl(caption = "Most Frequent Institutions for Packages on GitHub", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Institutions for Packages on GitHub

Institution	Sector	Freq
RStudio	Business	278
University of California-Berkeley	Academic	53
Harvard University	Academic	51
NetEase	Business	38
University of Wisconsin-Madison	Academic	36
University of Oslo	Academic	35
University of Michigan-Ann Arbor	Academic	33
University College London	Academic	32
University of Alberta	Academic	28
French National Centre for Scientific Research	Government	25

### Table output of top 10 Institutions for unique maintainers
top10_Institutions_GitHub_unique %>%
  kbl(caption = "Most Frequent Institutions for Unique Maintainers on GitHub", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Institutions for Unique Maintainers on GitHub

Institution	Sector	Freq
RStudio	Business	45
Harvard University	Academic	24
University of Michigan-Ann Arbor	Academic	22
NetEase	Business	22
University of Washington-Seattle Campus	Academic	19
University of California-Berkeley	Academic	14
University College London	Academic	14
University of Wisconsin-Madison	Academic	14
Copenhagen University	Academic	14
University of Oslo	Academic	12

3.1.2.3 How are these distributions changing over time?

We can identify the year created by looking at the date and time the repository was created on github. This is one of the variables we collected during GitHub data collection.

We can now see how the distribution of sectors is changing over time and also identify patterns in years where we were able to identify the most sectors. We do the same type of analysis, one for sectors of all R packages on GitHub and one for sectors of all unique R maintainers on GitHub .

It looks like the ability to identify a sector generally increases from year to year all the way up until 2020, where there is a dip in the number of packages and maintainers being registered on GitHub. As for the sector distribution, it essentially stays the same from year to year for both plots. Academic makes a majority of the distribution, while there are slight fluctuations in the other sectors.

cran_repos_time <- cran_repos %>%
  filter(Sector != "Unknown" & year_created != "NA" ) %>%
ggplot(aes(x = as.factor(year_created), fill = Sector)) +
  geom_bar() +
  labs(
    x = "Year",
    y = "Number of Packages",
    title = "Change in Sectors Over Time for R Packages on GitHub"
  ) +
  theme_clean()+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+
  ylim(c(0, 300))

cran_repos_time


cran_repos_unique_time <- cran_repos_unique %>%
  filter(Sector != "Unknown" & year_created != "NA" ) %>%
ggplot(aes(x = as.factor(year_created), fill = Sector)) +
  geom_bar() +
  labs(
    x = "Year",
    y = "Number of Maintainers",
    title = "Change in Sectors Over Time for Unique R Maintainers on GitHub"
  ) +
  theme_clean()+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))+
  ylim(c(0, 300))


cran_repos_unique_time

3.1.3 GitHub R Package Contributors

Now we look at distributions of all R contributors on GitHub. After GitHub data collection, we were able to identify 14,328 unique R contributors.

cran_users_unique <- cran_users %>%
                        distinct(login, .keep_all = T)

nrow(cran_users_unique)

[1] 14328

We also collected commit data for each of the unique R contributors. We join this back with our unique R contributors dataframe to combine commit, sector, country, and organization variables.

### summing up total commits for all unique users of unique repos
user_commits_total <- user_commits %>%
                  group_by(slug, login) %>%
                  summarise(total_additions = sum(additions)) %>%
                  ungroup()

### join back to unique users dataframe for other variables
user_commits_total <- user_commits_total %>%
                        left_join(cran_users_unique, by = "login") %>%
                          select(slug, login,name, email, total_additions, organization, sector, country)
cran_repos2 <- cran_repos %>%
                select(slug, year_created, stargazer_count, fork_count, Downloads_All_Time, Downloads_Normalized, Reverse_Depends_Count)

user_commits_total <- user_commits_total %>%
                        left_join(cran_repos2, by = "slug")

### Rename NA sectors to Unknown 
user_commits_total <- user_commits_total %>%
  mutate(sector = ifelse(is.na(sector) | sector == "Unknown", "Unknown", sector))

3.1.3.1 What is the distribution of unique GitHub R contributors by sector?

For the 14,328 unique R contributors on GitHub, we were able to identify a sector for 2,573 of them. 1870 coming from academic, 482 from business, 84 from government, and 137 from nonprofit

pander(table(cran_users_unique$sector, useNA = "always"))

Academic	Business	Government	Nonprofit	Unknown	NA
1870	482	84	137	11755	0

For unique R developers (contributors to a slug) on GitHub, 73% are identified as academic, 19% as business, 5% as nonprofit, and 3% as government.

# Calculate counts by sector (All packages on GitHub)
cran_user_sector_counts <- cran_users_unique %>%
  filter(sector != "NA" & sector != "Unknown") %>%
  count(sector) %>%
  mutate(proportion = n / sum(n),
         proportion_label = paste0(round(proportion * 100, 1), "%")) %>%
  arrange(desc(proportion)) %>%
  mutate(sector = factor(sector, levels = unique(sector)))

# Save plot
cran_user_sector_counts_plot <- ggplot(cran_user_sector_counts, aes(x = sector, y = n)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = proportion_label), vjust = -0.3) +
  ylab("Count of Developers") +
  ylim(c(0, 2000))+
  ggtitle(label = "Number of R Package Developers on GitHub by Sector")+ 
  labs(caption = "*Developers without sector information are removed in this figure (82% of 14,328 R Developers)")+
  theme_clean()

cran_user_sector_counts_plot

3.1.3.1.1 How do we attribute contribution to sectors (equal)?

We now aim to try to attribute contribution to sectors with a couple of methods. First, we look at equal contribution, where each member of a repository is given an equal fraction of credit regardless of level of contribution. So, if a repository has five members, each member will get .2 credit, and then the fractions are aggregated to the sectors. We will count the fraction to unknown sectors as well, but we will remove it in any graphical displays, as we already know this will be the highest percentage.

Note: This is different than looking at unique user distribution, as it will count repeat users if they are members of multiple repositories

# 1. Count the number of unique login per slug.
login_counts <- user_commits_total %>%
  group_by(slug) %>%
  summarise(num_logins = n_distinct(login))

# 2. Compute the contribution fraction for each login.
user_commits_total <- user_commits_total %>%
  left_join(login_counts, by = "slug") %>%
  mutate(contribution_fraction_equal = 1 / num_logins) %>%
  select(-num_logins)  # Removing the num_logins column as it's no longer needed

# 3. Sum the contribution fraction for each sector per slug.
sector_contribution <- user_commits_total %>%
  group_by(slug, sector) %>%
  summarise(total_contribution_fraction = sum(contribution_fraction_equal))

# 4. Aggregate the contribution fraction for each sector across all slugs.
sector_aggregated <- sector_contribution %>%
  group_by(sector) %>%
  summarise(overall_contribution_fraction = sum(total_contribution_fraction))


# Calculate the total overall contribution fraction over all sectors
total_overall_contribution = sum(sector_aggregated$overall_contribution_fraction)

# Calculate the percentage contribution for each sector
sector_aggregated = sector_aggregated %>%
  mutate(percentage_contribution = round((overall_contribution_fraction / total_overall_contribution) * 100, 1))

### Plot percentage contribution
sector_aggregated$percentage_label <- scales::percent(sector_aggregated$percentage_contribution / 100)

Based on equal contribution of each unique login to each unique repository, we would attribute 80% of credit to the academic sector, 15% to the business, 2% to the government, and 3% to the nonprofit. Note that we removed Unknown from the distribution, where we would have to attribute 78% to. So, the percentage distributions listed here are based on the percentage we do know.

### Excluding the unknown percentage in the table
total_excluding_unknown <- sum(sector_aggregated$overall_contribution_fraction[sector_aggregated$sector != "Unknown"])

### recalculating what percentages would be without unknown 
sector_aggregated <- sector_aggregated %>%
  mutate(percentage_contribution_excl_unknown = ifelse(sector != "Unknown", 
                                                       round((overall_contribution_fraction / total_excluding_unknown) * 100, 1), NA_real_))

### making labels
sector_aggregated$percentage_label_excl_unknown <- scales::percent(sector_aggregated$percentage_contribution_excl_unknown / 100, accuracy = 0.1)

ggplot(sector_aggregated %>% filter(sector != "Unknown"), aes(x = sector, y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = -0.5, size = 4) +
  geom_text(aes(label = paste0("(", round(overall_contribution_fraction, 2), ")")), position = position_dodge(width = 0.9), vjust = -2.5)+
  labs(title = "Percentage Contribution by Sector (Equal)",
       x = "Sector",
       y = "Percentage Contribution") +
  theme_clean() +
  labs(caption = "*Excludes the percentage contribution from unknown sector (77.7%)")+
  ylim(0,100)

3.1.3.1.2 How do we attribute contribution to sectors based on lines of code?

We also can attribute contribution to sectors based on the lines of code written for a unique user of a given repository. The more lines of code added for that repository, the more credit that user will get. So, if a repository has 500 total lines of code, and one user wrote 300 of them, he/she would get .6 of the credit. We again apply the fractional counting method to the sectors after calculating this.

# Calculate the total code additions for each slug (project/repository identifier)
# Grouping by the slug, and then summarizing the total additions for each slug.
slug_totals <- user_commits_total %>%
  group_by(slug) %>%
  summarise(total_code_for_slug = sum(total_additions))

# Compute the contribution fraction for each user.
# This is done by joining the user's total additions with the total code additions for their respective slug,
# and then computing the user's contribution as a fraction of the slug's total.
user_commits_total <- user_commits_total %>%
  left_join(slug_totals, by = "slug") %>%
  mutate(contribution_fraction_loc = total_additions / total_code_for_slug)

# Compute the total contribution fraction for each combination of slug and sector.
# This groups the data by slug and sector, and then sums up the contribution fractions.
sector_addition_contribution <- user_commits_total %>%
  group_by(slug, sector) %>%
  summarise(total_addition_contribution = sum(contribution_fraction_loc))

# Aggregate the contributions at the sector level.
# This groups by the sector and then computes the overall contribution fraction for each sector.
sector_aggregated_additions <- sector_addition_contribution %>%
  group_by(sector) %>%
  summarise(overall_addition_contribution = sum(total_addition_contribution, na.rm = TRUE))

# Compute the total overall additions across all sectors.
total_overall_additions = sum(sector_aggregated_additions$overall_addition_contribution)

# Calculate the percentage of additions for each sector relative to the total overall additions.
sector_aggregated_additions$percentage_additions = round((sector_aggregated_additions$overall_addition_contribution / total_overall_additions) * 100,1)

# Create a label for the percentage values, turning the decimal fraction into a percentage string (e.g., 0.5 becomes "50%").
sector_aggregated_additions$percentage_label_additions = scales::percent(sector_aggregated_additions$percentage_additions / 100)

After doing these calculations, we now see that 83% can be attributed to the academic sector, 13% to the business, 2% to the government, and 2% to the nonprofit. The original amount attributed to Unknown decreased to 75.6%

# Calculate the total code additions while excluding the 'Unknown' sector.
total_excluding_unknown_add <- sum(sector_aggregated_additions$overall_addition_contribution[sector_aggregated_additions$sector != "Unknown"])

# Compute the percentage contribution for each sector relative to the total (excluding 'Unknown' sector).
# If the sector is 'Unknown', set the percentage as NA.
sector_aggregated_additions <- sector_aggregated_additions %>%
  mutate(percentage_contribution_excl_unknown = ifelse(sector != "Unknown", 
                                                       round((overall_addition_contribution / total_excluding_unknown_add) * 100, 1), NA_real_))

# Create a label for the percentage values that excludes 'Unknown' sector, turning the decimal fraction into a percentage string.
 sector_aggregated_additions$percentage_label_excl_unknown <- scales::percent(sector_aggregated_additions$percentage_contribution_excl_unknown / 100, accuracy = 0.1)

# Visualize data
ggplot(sector_aggregated_additions %>% filter(sector != "Unknown"), aes(x = sector, y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = -0.5, size = 4) + # Adjust vjust and size as needed
  geom_text(aes(label = paste0("(", round(overall_addition_contribution, 2), ")")), position = position_dodge(width = 0.9), vjust = -2.5)+
  labs(#title = "Percentage Contribution by Sector (Weighted)",
       x = "Sector",
       y = "Percentage Contribution") +
  theme_clean() +
  #labs(caption = "*Excludes the percentage contribution from unknown sector (75.6%)")+
  ylim(0,100)+
  theme(axis.text = element_text(size = 14),
         axis.title = element_text(size = 12))

3.1.3.1.3 Attributing credit to Sectors over time

# Compute the total contribution fraction for each combination of slug, sector, and year
sector_addition_contribution_time <- user_commits_total %>%
  group_by(slug, sector, year_created) %>%
  summarise(total_addition_contribution = sum(contribution_fraction_loc), .groups = 'drop')

# Aggregate the contributions at the sector and year level
sector_aggregated_additions_time <- sector_addition_contribution_time %>%
  group_by(sector, year_created) %>%
  summarise(overall_addition_contribution = sum(total_addition_contribution, na.rm = TRUE), .groups = 'drop')

# Compute the total overall additions across all sectors by year
total_overall_additions_by_year <- sector_aggregated_additions_time %>%
  group_by(year_created) %>%
  summarise(yearly_total = sum(overall_addition_contribution), .groups = 'drop')

# Calculate the percentage of additions for each sector relative to the total overall additions for each year
sector_aggregated_additions_time <- sector_aggregated_additions_time %>%
  left_join(total_overall_additions_by_year, by = "year_created") %>%
  mutate(percentage_additions = (overall_addition_contribution / yearly_total) * 100)

# Calculate the total code additions for each year while excluding the 'Unknown' sector
total_excluding_unknown_by_year <- sector_aggregated_additions_time %>%
  filter(sector != "Unknown") %>%
  group_by(year_created) %>%
  summarise(yearly_total_excl_unknown = sum(overall_addition_contribution), .groups = 'drop')

# Compute the percentage contribution for each sector by year relative to the year's total excluding 'Unknown'
sector_aggregated_additions_time <- sector_aggregated_additions_time %>%
  left_join(total_excluding_unknown_by_year, by = "year_created") %>%
  mutate(percentage_contribution_excl_unknown = ifelse(sector != "Unknown" & !is.na(yearly_total_excl_unknown), 
                                                       (overall_addition_contribution / yearly_total_excl_unknown) * 100, 
                                                       NA_real_))

# Round the percentages and create labels
sector_aggregated_additions_time$percentage_contribution_excl_unknown <- round(sector_aggregated_additions_time$percentage_contribution_excl_unknown, 1)
sector_aggregated_additions_time$percentage_label_excl_unknown <- ifelse(is.na(sector_aggregated_additions_time$percentage_contribution_excl_unknown),
                                                                    NA_character_,
                                                                    percent(sector_aggregated_additions_time$percentage_contribution_excl_unknown / 100))

# Assuming sector_aggregated_additions contains the necessary processed data
# Filter out the 'Unknown' sector for plotting
plot_data <- sector_aggregated_additions_time %>%
  filter(sector != "Unknown",
         year_created != "NA" & year_created != "2023")

The following graph shows fractional credit for sectors over time.

# Stacked Bar Chart for Yearly Totals
R_Sectors_time <- ggplot(plot_data, aes(x = year_created, y = overall_addition_contribution, fill = sector)) +
  geom_bar(stat = "identity") +
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  labs(x = "", y = "Fractional Count of Packages", title = "Fractional Count of Packages for Sector by Year") + # Fractional Count of Packages for Sector by Year, y-axis: Fractional Count of Packages
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom")

R_Sectors_time

# ggsave(filename = "\\\\westat.com\\dfs\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\New Graphs\\R_Sectors_time.png", plot = R_Sectors_time, width = 8, height = 6, dpi = 300)

# Line Chart for Percentages by Sector (excluding 'Unknown')
ggplot(plot_data,
       aes(x = year_created, y = percentage_contribution_excl_unknown, color = sector, group = sector)) +
  geom_line() +
  geom_point() +
  labs(x = "", y = "Percentage of Total Packages", title = "Weighted Sector Contribution by Year") +
  scale_color_westat(option = "BLUES", drop = FALSE) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "bottom")

# Create the stacked bar plot
ggplot(plot_data, aes(x = year_created, y = percentage_contribution_excl_unknown, fill = sector)) +
  geom_bar(stat = "identity") +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "", y = "Percentage Contribution", title = "Weighted Sector Contribution by Year") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),  # Adjust the angle of the x-axis labels for readability
        legend.position = "bottom")  # Place the legend at the bottom

3.1.3.2 What is the distribution of GitHub R contributors by country?

The diverstidy function, which we use to extract country from a user, can supply some messy data in terms of identifying multiple countries for a unique user. We first need to clean that up before analyzing country distributions. There were 427 unique users that had multiple countries supplied, so we manually went through and decided whether all countries should be kept, or some should be deleted. The country extracted can be based on email, location, company, or an organization that a given user has listed.

We filter out NA values here and replace with “Unknown”

cran_users_unique <- cran_users_unique %>%
  mutate(
    country_fixed = strsplit(as.character(country), split = "\\|") %>%   # Split on "|"
      map(~unique(.)) %>%                                         # Keep only unique values
      sapply(paste, collapse = ",")                               # Collapse back into a string
  )

cran_users_unique <- cran_users_unique %>%
  mutate(country_fixed = ifelse(country_fixed == "NA", NA_character_, country_fixed))

cran_users_unique <- cran_users_unique %>%
  mutate(
    country_fixed = strsplit(country_fixed, split = ",") %>%  # Split on comma
      map(~ .[!. %in% "NA"]) %>%                             # Remove "NA" values (note the space before "NA")
      sapply(paste, collapse = ",")                           # Collapse back into a string
  )


cran_users_unique <- cran_users_unique %>%
                            left_join(user_countries, by = "login")

cran_users_unique <- cran_users_unique %>%
  mutate(country_final = ifelse(is.na(country_final), country_fixed, country_final))

cran_users_unique <- cran_users_unique %>%
  mutate(country_final = ifelse(is.na(country_final) | country_final == "NA", "Unknown", country_final))

Based on the unique R GitHub users, the United States is the most frequent country found followed by Germany and the United Kingdom. Out of 14,328 unique users, there were 5575 that we were unable to find a country for.

### sum of Unknowns for country

sum(cran_users_unique$country_final == "Unknown")

[1] 5575

### sorting to the top 10 most common countries for distinct GitHub users
top10_Countries_GitHub_users_unique <- cran_users_unique %>%
  filter(country_final != "Unknown")

top10_Countries_GitHub_users_unique <-  sort(table(top10_Countries_GitHub_users_unique$country_final), decreasing = T)

top10_Countries_GitHub_users_unique  <- as.data.frame(head(top10_Countries_GitHub_users_unique , 10))

colnames(top10_Countries_GitHub_users_unique ) <- c("country_final", "Freq")

 ### Graph output of top 10 countries for unique maintainers
 ggplot(top10_Countries_GitHub_users_unique , aes(x = reorder(country_final, Freq), y = Freq))+
   geom_bar(stat = "identity", fill = westat_blue()) +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of GitHub Users",
         title = "Top 10 Countries for R Users on GitHub" ) +
    ylim(c(0, 3000))+
  scale_fill_westat(option = "BLUES")+
  theme_clean()+
   theme(
  plot.title = element_text(size = 13))+
   labs(caption = "*Excludes count from unknown countries (5575)")

### Table output of top 10 Institutions for packages
top10_Countries_GitHub_users_unique %>%
  kbl(caption = "Most Frequent Countries for R Developers on GitHub", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Countries for R Developers on GitHub

country_final	Freq
United States	2809
Germany	854
United Kingdom	660
Canada	410
France	352
Australia	322
China	286
Netherlands	264
Switzerland	226
India	214

3.1.3.2.1 How do we attribute contribution to countries (equal)?

As stated prior, there are some logins that have multiple countries listed. For these logins, we split the contribution fractions for equal and lines of code equally among the countries. So, if a user had two countries in a slug with 4 unique users, each country will get .125 credit based on equal contribution. For lines of code, if that user had 500 additions, each country would get 250 additions. After doing this, we see that there are 123 unique countries identified.

# Function to handle the splitting and division for multiple countries
process_multiple_countries <- function(df) {
  num_countries <- length(str_split(df$country_final, ",\\s*")[[1]])
  df %>%
    separate_rows(country_final, sep = ",\\s*") %>%
    mutate(
      total_additions = total_additions / num_countries,
      contribution_fraction_equal = contribution_fraction_equal / num_countries,
      contribution_fraction_loc = contribution_fraction_loc / num_countries
    )
}

# join country variable back to commit table
user_countries <- cran_users_unique %>%
                    select(login, country_final)

user_commits_total <- user_commits_total %>%
                            left_join(user_countries, by = "login")

# Replace NA values in 'country_final' with 'Unknown'
user_commits_total$country_final[is.na(user_commits_total$country_final)] <- "Unknown"

# Process rows with multiple countries
multi_country_rows <- user_commits_total %>%
  filter(str_detect(country_final, ",")) %>%
  group_by(login) %>%
  do(process_multiple_countries(.))

# Exclude multi-country rows from the original df and bind the processed rows
user_commits_total <- user_commits_total %>%
  filter(!str_detect(country_final, ",")) %>%
  bind_rows(multi_country_rows)

Instead of grouping by sector, we have to group by country here.

# Sum the contribution fraction for each sector per slug.
country_contribution <- user_commits_total %>%
  group_by(slug, country_final) %>%
  summarise(total_contribution_fraction = sum(contribution_fraction_equal))

# Aggregate the contribution fraction for each country across all slugs
country_aggregated <- country_contribution %>%
  group_by(country_final) %>%
  summarise(overall_contribution_fraction = sum(total_contribution_fraction))

# Calculate the total overall contribution fraction over all countries
total_overall_contribution = sum(country_aggregated$overall_contribution_fraction)

# Calculate the percentage contribution for each country
country_aggregated = country_aggregated %>%
  mutate(percentage_contribution = round((overall_contribution_fraction / total_overall_contribution) * 100, 1))

### Plot percentage contribution
country_aggregated$percentage_label <- scales::percent(country_aggregated$percentage_contribution / 100)

If we give equal contributions to countries, then the United states would get 31.1% of the credit followed by Germany with 10.9% credit. This excludes the contribution counted towards unknown (38.5%), so these percentages are based on the percentage that we know (61.5%).

total_excluding_unknown <- sum(country_aggregated$overall_contribution_fraction[country_aggregated$country_final != "Unknown"])

country_aggregated <- country_aggregated %>%
  mutate(percentage_contribution_excl_unknown = ifelse(country_final != "Unknown", 
                                                       round((overall_contribution_fraction / total_excluding_unknown) * 100, 1), NA_real_))


country_aggregated$percentage_label_excl_unknown <- scales::percent(country_aggregated$percentage_contribution_excl_unknown / 100, accuracy = 0.1)


top_10_countries <- country_aggregated %>%
  arrange(desc(percentage_contribution_excl_unknown)) %>%
  head(10)

ggplot(top_10_countries, aes(x = reorder(country_final, percentage_contribution_excl_unknown),  y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = .5, size = 4, hjust = -.25) +
  geom_text(aes(label = paste0("(", round(overall_contribution_fraction, 2), ")")), position = position_dodge(width = 0.9), vjust = .25, hjust = -1)+# Adjust vjust and size as needed
  labs(title = "Percentage Contribution by Country (Equal - Top 10 Countries)",
       x = "Country",
       y = "Percentage Contribution") +
  theme_clean() +
  ylim(0,100)+
  coord_flip()+
  theme(plot.title = element_text(size = 10))+
  labs(caption = "*Excludes the percentage contribution from unknown countries (38.5%)")

3.1.3.2.2 How do we attribute contribution to countries based on lines of code?

Now, we base the contribution on additions for country just as we did for sector.

country_addition_contribution <- user_commits_total %>%
  group_by(slug, country_final) %>%
  summarise(total_addition_contribution = sum(contribution_fraction_loc))

country_aggregated_additions <- country_addition_contribution %>%
  group_by(country_final) %>%
  summarise(overall_addition_contribution = sum(total_addition_contribution, na.rm = TRUE))

total_overall_additions = sum(country_aggregated_additions$overall_addition_contribution)

country_aggregated_additions$percentage_additions = round((country_aggregated_additions$overall_addition_contribution / total_overall_additions) * 100,1)

country_aggregated_additions$percentage_label_additions = scales::percent(country_aggregated_additions$percentage_additions / 100)

Based on additions, the percentage attributed towards unknwon decreases to 34.1%, so the percentage that we know increases to 65.9% overall. United states still is at the top, but it decreases slightly to 30.9%. The top 10 and the order of the top 10 stays the same, but the percentages increase slightly for the ones more towards the bottom.

total_excluding_unknown <- sum(country_aggregated_additions$overall_addition_contribution[country_aggregated_additions$country_final != "Unknown"])

country_aggregated_additions <- country_aggregated_additions %>%
  mutate(percentage_contribution_excl_unknown = ifelse(country_final != "Unknown", 
                                                       round((overall_addition_contribution / total_excluding_unknown) * 100, 1), NA_real_))


country_aggregated_additions$percentage_label_excl_unknown <- scales::percent(country_aggregated_additions$percentage_contribution_excl_unknown / 100, accuracy = 0.1)

top_10_countries_additions <- country_aggregated_additions %>%
  arrange(desc(percentage_contribution_excl_unknown)) %>%
  head(10)

 ggplot(top_10_countries_additions, aes(x = reorder(country_final, percentage_contribution_excl_unknown),  y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = .5, size = 6, hjust = -.12) + 
  geom_text(aes(label = paste0("(", round(overall_addition_contribution, 2), ")")), position = position_dodge(width = 0.9), vjust = .5, hjust = -1.1, size = 5)+# Adjust vjust and size as needed
  labs( x = "",
       y = "Percentage Contribution") +
  theme_clean() +
  ylim(0, 100)+
  coord_flip()+
  theme(axis.text = element_text(size = 14),
         axis.title = element_text(size = 12))

3.1.3.2.3 Attributing Credit to Countries over time

# Step 1: Sum the contribution fraction for each country per slug, per year
country_contribution_by_year <- user_commits_total %>%
  group_by(slug, country_final, year_created) %>%
  summarise(total_contribution_fraction = sum(contribution_fraction_loc, na.rm =  T), .groups = 'drop')

# Step 2: Aggregate the contribution fraction for each country by year
country_aggregated_by_year <- country_contribution_by_year %>%
  group_by(country_final, year_created) %>%
  summarise(overall_contribution_fraction = sum(total_contribution_fraction), .groups = 'drop')

# Step 3: Exclude 'Unknown' and determine the top ten countries for each year
country_aggregated_by_year_excl_unknown <- country_aggregated_by_year %>%
  filter(country_final != "Unknown")

# Step 4: Calculate the total overall contribution by year, excluding 'Unknown'
total_overall_contribution_by_year_excl_unknown <- country_aggregated_by_year_excl_unknown %>%
  group_by(year_created) %>%
  summarise(yearly_total_excl_unknown = sum(overall_contribution_fraction), .groups = 'drop')

# Now compute the percentage of contribution for each of the top countries, excluding 'Unknown'
country_aggregated_by_year_excl_unknown <- country_aggregated_by_year_excl_unknown %>%
  left_join(total_overall_contribution_by_year_excl_unknown, by = "year_created") %>%
  mutate(percentage_contribution_excl_unknown = (overall_contribution_fraction / yearly_total_excl_unknown) * 100) %>%
  arrange(year_created, desc(percentage_contribution_excl_unknown))

# Step 5: Get the top ten countries by year, excluding 'Unknown'
top_countries_by_year_excl_unknown <- country_aggregated_by_year_excl_unknown %>%
  group_by(year_created) %>%
  top_n(10, wt = percentage_contribution_excl_unknown) %>%
  ungroup()

# Filter out the 'Unknown' sector for plotting
plot_data2 <- top_countries_by_year_excl_unknown %>%
  filter(year_created != "NA" & year_created != "2023")


plot_data3 <- top_countries_by_year_excl_unknown %>%
  filter(year_created != "NA" & year_created != "2023",
         country_final %in% c("United States", "Germany", "United Kingdom", "France", "Canada", "Australia", "Netherlands", "Switzerland", "Spain", "China"))

The following graph shows all countries fractional credit over time

my_colors <- c("#FF0000", "#00FF00", "#0000FF", "#FFFF00", "#FF00FF", "#00FFFF", "#000000",
               "#800000", "#008000", "#000080", "#808000", "#800080", "#008080", "#808080",
               "#C00000", "#00C000", "#0000C0", "#C0C000", "#C000C0", "#00C0C0",
               "#400000", "#004000", "#000040", "#404000", "#400040") # Define more colors as needed


# Stacked Bar Chart for Yearly Totals
ggplot(plot_data2, aes(x = year_created, y = overall_contribution_fraction, fill = country_final)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = my_colors) +
  labs(x = "", y = "Number of Packages", title = "Weighted Country Contribution by Year", fill = "Country") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom")

This subsets to the top 10 that we identified previously

my_colors <- c("#6B8E23", "#8FBC8F", "#2E8B57", "#4682B4", "#87CEEB",
               "#4169E1", "#B0C4DE", "#D2691E", "#CD853F", "#F4A460")





# Stacked Bar Chart for Yearly Totals
R_Country_time <- ggplot(plot_data3, aes(x = year_created, y = overall_contribution_fraction, fill = country_final)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = my_colors) +
  labs(x = "", y = "Fractional Count of Packages", title = "Top Countries by Fractional Count of Packages", fill = "Country") + # Top Countries by Fractional Count of Packages, y-axis: Fractional Count of Packages
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom")

R_Country_time

## ggsave(filename = "\\\\westat.com\\dfs\\DVSTAT\\Individual Directories\\Askew\\Paper_Data\\New Graphs\\R_Country_time.png", plot = R_Country_time, width = 8, height = 6, dpi = 300)

3.1.3.3 What is the distribution of unique GitHub R contributors by organization?

We also have the organization variable for some users. It works with the sector variable, so if we were not able to identify a sector, we also were not able to identify an organization.

# Replace NA values in 'organization' with 'Unknown'
user_commits_total$organization[is.na(user_commits_total$organization)] <- "Unknown"
user_commits_total$organization[user_commits_total$organization == "NA"] <- "Unknown"
cran_users_unique$organization[cran_users_unique$organization == "NA"] <- "Unknown"

If we look at the top 10 most frequent organizations for unique R developers on Github, Google has the most with 86 followed by NetEase with 57. Only one in the top 10 is from a sector other than business or academic (Broad Institute - nonprofit)

cran_users_unique <- cran_users_unique %>%
                          filter(organization != "Unknown")

### sorting to the top 10 most common institutions for distinct GitHub users
top10_Institutions_GitHub_users_unique <- sort(table(cran_users_unique$organization), decreasing = T)


top10_Institutions_GitHub_users_unique <- as.data.frame(head(top10_Institutions_GitHub_users_unique, 10))

colnames(top10_Institutions_GitHub_users_unique) <- c("organization", "Freq")

### joining to institution unique dataframe to get sector variable
top10_Institutions_GitHub_users_unique <- cran_users_unique %>% 
  right_join(top10_Institutions_GitHub_users_unique, by = "organization")%>%
  distinct(organization, .keep_all = T)%>%
  select(organization, sector, Freq)%>%
  arrange(desc(Freq))

 ### Graph output of top 10 institutions for unique maintainers
 ggplot(top10_Institutions_GitHub_users_unique, aes(x = reorder(organization, Freq), y = Freq, fill = sector))+
   geom_bar(stat = "identity") +
    coord_flip() +
    scale_y_continuous(expand = c(0,0)) +
    labs(x = "", y = "Number of GitHub Users",
         title = "Top 10 Organizations for Unique R Users on GitHub" ) +
    ylim(c(0, 200))+
  scale_fill_westat(option = "BLUES", drop = FALSE)+
  theme_clean()+
   theme(
  plot.title = element_text(size = 13))+
   labs(caption = "*Those without org info are removed in this figure (82% of 14,328 R Developers)")

### Table output of top 10 Institutions for packages
top10_Institutions_GitHub_users_unique %>%
  kbl(caption = "Most Frequent Institutions for R Developers on GitHub", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box()

Most Frequent Institutions for R Developers on GitHub

organization	sector	Freq
Google	Business	86
NetEase	Business	57
University of California-Berkeley	Academic	46
Broad Institute	Nonprofit	45
University of Michigan-Ann Arbor	Academic	41
University of Washington-Seattle Campus	Academic	40
Harvard University	Academic	40
Microsoft	Business	40
RStudio	Business	33
Smith College	Academic	32

3.1.3.3.1 How do we attribute contribution to organization (equal)?

We now will look at equal contribution for organizations

# Sum the contribution fraction for each organization per slug.
org_contribution <- user_commits_total %>%
  group_by(slug, organization) %>%
  summarise(total_contribution_fraction = sum(contribution_fraction_equal))

# Aggregate the contribution fraction for organization across all slugs
org_aggregated <- org_contribution %>%
  group_by(organization) %>%
  summarise(overall_contribution_fraction = sum(total_contribution_fraction))

# Calculate the total overall contribution fraction over all organizations
total_overall_contribution = sum(org_aggregated$overall_contribution_fraction)

# Calculate the percentage contribution for each organization
org_aggregated = org_aggregated %>%
  mutate(percentage_contribution = round((overall_contribution_fraction / total_overall_contribution) * 100, 1))

### Plot percentage contribution
org_aggregated$percentage_label <- scales::percent(org_aggregated$percentage_contribution / 100)

Again, the equal percentage contribution to Unknown is 77.7% just like we saw in the sector contribution section. Of the percentage we do know (611 different organizations), Rstudio leads with 7.2% followed by UCLA with 3%

total_excluding_unknown <- sum(org_aggregated$overall_contribution_fraction[org_aggregated$organization != "Unknown"])

org_aggregated <- org_aggregated %>%
  mutate(percentage_contribution_excl_unknown = ifelse(organization != "Unknown", 
                                                       round((overall_contribution_fraction / total_excluding_unknown) * 100, 1), NA_real_))


org_aggregated$percentage_label_excl_unknown <- scales::percent(org_aggregated$percentage_contribution_excl_unknown / 100, accuracy = 0.1)


top_10_orgs <- org_aggregated %>%
  arrange(desc(percentage_contribution_excl_unknown)) %>%
  head(10)

ggplot(top_10_orgs, aes(x = reorder(organization, percentage_contribution_excl_unknown),  y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = .5, size = 4, hjust = -.25) +
    geom_text(aes(label = paste0("(", round(overall_contribution_fraction, 2), ")")), position = position_dodge(width = 0.9), vjust = .25, hjust = -1)+# Adjust vjust and size as needed
  labs(title = "Percentage Contribution by Organization (Equal - Top 10 Organizations)",
       x = "Organization",
       y = "Percentage Contribution") +
  theme_clean() +
  ylim(0,100)+
  coord_flip()+
  theme(plot.title = element_text(size = 7))+
  labs(caption = "*Excludes the percentage contribution from unknown organizations (77.7%)")

3.1.3.3.2 How do we attribute contribution to organizations based on lines of code?

Now, we base the contribution on additions for organization just as we did for sector and country.

org_addition_contribution <- user_commits_total %>%
  group_by(slug, organization) %>%
  summarise(total_addition_contribution = sum(contribution_fraction_loc))

org_aggregated_additions <- org_addition_contribution %>%
  group_by(organization) %>%
  summarise(overall_addition_contribution = sum(total_addition_contribution, na.rm = TRUE))

total_overall_additions = sum(org_aggregated_additions$overall_addition_contribution)

org_aggregated_additions$percentage_additions = round((org_aggregated_additions$overall_addition_contribution / total_overall_additions) * 100,1)

org_aggregated_additions$percentage_label_additions = scales::percent(org_aggregated_additions$percentage_additions / 100) 

Based on additions, the percentage contribution towards unknown is 75.6% just as we saw for sector, which is what we expect because the two variables coincide with one another. The percentage coming from Rstudio decreases to 5.9% (still number one), and the top 10 along with the order of the top 10 changes slightly. Notably, Monash University moves from the 10th position to the 4th position when factoring in additions.

total_excluding_unknown <- sum(org_aggregated_additions$overall_addition_contribution[org_aggregated_additions$organization != "Unknown"])

org_aggregated_additions <- org_aggregated_additions %>%
  mutate(percentage_contribution_excl_unknown = ifelse(organization != "Unknown", 
                                                       round((overall_addition_contribution / total_excluding_unknown) * 100, 1), NA_real_))


org_aggregated_additions$percentage_label_excl_unknown <- scales::percent(org_aggregated_additions$percentage_contribution_excl_unknown / 100, accuracy = 0.1)


top_10_orgs_additions <- org_aggregated_additions %>%
  arrange(desc(percentage_contribution_excl_unknown)) %>%
  head(10)

ggplot(top_10_orgs_additions, aes(x = reorder(organization, percentage_contribution_excl_unknown),  y = percentage_contribution_excl_unknown)) +
  geom_bar(stat = "identity", fill = westat_blue()) +
  geom_text(aes(label = percentage_label_excl_unknown), vjust = .5, size = 4, hjust = -.25) +
    geom_text(aes(label = paste0("(", round(overall_addition_contribution, 2), ")")), position = position_dodge(width = 0.9), vjust = .25, hjust = -1)+# Adjust vjust and size as needed
  labs(title = "Percentage Contribution by Organization (Weighted - Top 10 Organizations)",
       x = "Organization",
       y = "Percentage Contribution") +
  theme_clean() +
  ylim(0,100)+
  coord_flip()+
  theme(plot.title = element_text(size = 7))+
  labs(caption = "*Excludes the percentage contribution from unknown (75.6%)")

3.2 Network Analysis

We create edgelists for countries and sectors in the following section

3.2.1 Countries

• What are the overall structural features of the OSS networks? How do they differ across fields, sectors, institutions, and countries? Units of analysis (OSS actors): projects, categories, developers, institutions, sectors, countries

• What are the different communities that can be identified using structural features of the networks? Do they correspond to similarities in languages, methods, location, culture?

### select dependency information for slugs and packages
cran_github_rdi <- cran_github %>%
                      select(Package, slug, Depends)

### rename columns
colnames(cran_github_rdi) <- c("Citing_Package", "slug", "Dependencies")


### Package citation column will be the unlisted dependencies column
cran_github_rdi$Package_Citation <- cran_github_rdi$Dependencies


### join commits information for the citing packages
cran_github_RDI <- cran_github_rdi %>%
                      inner_join(user_commits_total, by = "slug")%>%
                        select(Citing_Package, slug, Dependencies, login,
                              country_final, total_additions, total_code_for_slug,
                              contribution_fraction_loc, Package_Citation) %>%
                       # Remove rows with NA in Depends
                        filter(!is.na(Package_Citation))

### rename columns on the basis of the citing package
colnames(cran_github_RDI) <- c("Citing_Package", "Citing_Slug", "Dependencies", "Citing_Login",  "Citing_Country",
                                "Citing_Additions", "Citing_Total_Slug_Additions", "Citing_Package_Fraction" , "Package_Citation")


### unlist the dependencies for joining
cran_github_RDI_network <-  cran_github_RDI %>%
  separate_rows(Package_Citation, sep = ",\\s*") %>%
  filter(Package_Citation != "")


#### prepare commits information for cited packages
user_commits_rdi <- user_commits_total %>%
  mutate(Package_Citation = str_split(slug, "/", simplify = TRUE)[, 2])%>%
  select(login, country_final, total_additions, total_code_for_slug, contribution_fraction_loc, Package_Citation)
  
  colnames(user_commits_rdi) <- c( "Cited_Login", "Cited_Country", 
                                   "Cited_Additions", "Cited_Total_Slug_Additions", "Cited_Package_Fraction", "Package_Citation")
  
  ### join cited package commit information to citing package dataframe
  cran_github_rdi_full <- cran_github_RDI_network %>%
                                        inner_join(user_commits_rdi, by = "Package_Citation")

  ### create dependency_fraction = citing package fraction multiplied by cited package fraction 
  cran_github_rdi_grouped <- cran_github_rdi_full %>%
  mutate(Dependency_Fraction = Citing_Package_Fraction * Cited_Package_Fraction)

# Group by Cited Country and Citing Country, and sum Dependency_Fraction

### the number of citations made from one country to another is simply the sum of the fractioned scores associated with each pair, with the sum across all possible pairs adding up to the total number of citations made at the world level.

dependency_summary <- cran_github_rdi_grouped %>%
  group_by(Cited_Country, Citing_Country) %>%
  summarize(Total_Dependency_Fraction = sum(Dependency_Fraction, na.rm = TRUE))

sum(dependency_summary$Total_Dependency_Fraction)

[1] 589

# Group by Cited Country and sum Total_Dependency_Fraction - total number of citations attributed to each country
citations_by_country <- dependency_summary %>%
  group_by(Cited_Country) %>%
  summarize(Fraction_of_Citations = round(sum(Total_Dependency_Fraction, na.rm = TRUE), 4))


sum(citations_by_country$Fraction_of_Citations)

[1] 589.0001

citations_by_country$Denominator_RDI <- round(citations_by_country$Fraction_of_Citations / sum(citations_by_country$Fraction_of_Citations),4)

# Group by citing country and sum Total_Dependency_Fraction - total number of citations made by each country
citings_by_country <- dependency_summary %>%
  group_by(Citing_Country) %>%
  summarize(Fraction_of_Citings = round(sum(Total_Dependency_Fraction, na.rm = TRUE), 4))


sum(citings_by_country$Fraction_of_Citings)

[1] 588.9999

# join citings by country with dependency_summary

citings_dependency_summary <- citings_by_country %>%
                                full_join(dependency_summary, by = "Citing_Country")

citings_dependency_summary$Numerator_RDI <- round(citings_dependency_summary$Total_Dependency_Fraction / citings_dependency_summary$Fraction_of_Citings,4)

## join denominator_RDI

citations_citings_dependency_summary <- citations_by_country %>%
                                full_join(citings_dependency_summary, by = "Cited_Country") %>%
                                select(Citing_Country, Cited_Country, Numerator_RDI, Denominator_RDI)

citations_citings_dependency_summary$RDI <- round(citations_citings_dependency_summary$Numerator_RDI / citations_citings_dependency_summary$Denominator_RDI,4)

dependency_summary %>%
  arrange(desc(Total_Dependency_Fraction))%>%
  kbl(caption = "Country Pair Dependency Weights", escape = F)%>%
  kable_classic()%>%
  kable_styling(font_size = 12, full_width = T)%>%
 row_spec(0, bold = T, background = westat_blue(), color = "white")%>%
  column_spec(1:2, border_right = T)%>%
  scroll_box(width = "100%", height = "500px")

Country Pair Dependency Weights

Cited_Country	Citing_Country	Total_Dependency_Fraction
Unknown	Unknown	87.3146359
United States	Unknown	64.3139658
United States	United States	35.7966513
Unknown	United States	26.3714621
France	Unknown	14.4800845
Unknown	Germany	12.1686281
Unknown	United Kingdom	11.7170320
Germany	Unknown	10.3942012
Norway	Norway	8.2998583
United States	Germany	7.9622908
Germany	Germany	7.6496787
Unknown	Netherlands	6.8692472
United States	Spain	6.8043969
Denmark	Unknown	6.7001129
Unknown	Canada	6.3906341
Canada	Unknown	6.3692538
United States	Italy	6.1850725
United States	Australia	6.1608042
Unknown	France	5.9665531
Unknown	Australia	5.7756594
Bulgaria	Unknown	5.2967973
United States	United Kingdom	5.1454252
United States	France	4.8013768
Germany	United States	4.7021219
Unknown	New Zealand	4.4227168
Unknown	Belgium	4.1871672
United Kingdom	Unknown	4.1108012
Netherlands	Unknown	3.6034512
Austria	United States	3.5561262
Unknown	Italy	3.3489010
Denmark	Denmark	3.1556103
France	United States	3.1419765
Unknown	Poland	3.0670811
Germany	Canada	2.8483983
Denmark	United States	2.6590604
Australia	Unknown	2.6535606
Unknown	Brazil	2.6226700
United States	Netherlands	2.6049659
Australia	Australia	2.5753766
United States	Canada	2.5707256
Canada	Canada	2.5509336
Germany	Australia	2.5390527
Norway	Unknown	2.4723814
Denmark	Germany	2.3466958
France	France	2.1811472
United States	Switzerland	2.1505801
United States	South Korea	2.0812051
Unknown	Ireland	2.0721542
Unknown	Denmark	2.0536379
United States	New Zealand	2.0487277
United States	Ireland	2.0308880
United Kingdom	Netherlands	2.0298811
United States	Brazil	2.0081528
Italy	Italy	2.0003613
Hong Kong	Hong Kong	2.0000000
Romania	Romania	2.0000000
Unknown	Sweden	1.9932171
Australia	United States	1.9867464
Israel	Unknown	1.9366085
United States	Denmark	1.9037860
Canada	United States	1.8759120
Germany	Spain	1.8035616
Germany	United Kingdom	1.7853307
Colombia	Unknown	1.7366579
Netherlands	Netherlands	1.7265773
United States	Peru	1.6861346
Netherlands	Germany	1.6542048
United Kingdom	United States	1.6175734
Norway	United States	1.5929450
Netherlands	United States	1.5654457
Germany	Netherlands	1.5589904
Germany	Israel	1.5411524
Spain	Ecuador	1.4848368
United States	Belgium	1.4739769
United States	China	1.4634399
United Kingdom	United Kingdom	1.4321661
Denmark	United Kingdom	1.4135857
Unknown	Colombia	1.3794315
United States	Mauritius	1.3733200
Canada	United Kingdom	1.3526687
United States	Romania	1.3411052
Unknown	Spain	1.3233865
Germany	Norway	1.3159755
France	Netherlands	1.3057481
United States	Austria	1.2618838
Canada	New Zealand	1.2552483
France	Spain	1.2341293
Bulgaria	Denmark	1.2013438
United States	Czech Republic	1.1903569
Unknown	Peru	1.1818778
Spain	Unknown	1.1622256
Japan	Unknown	1.1621650
Spain	Italy	1.1597043
Denmark	Netherlands	1.1553843
United States	Taiwan	1.1521244
Sweden	United States	1.1132434
Switzerland	United Kingdom	1.0602851
France	United Kingdom	1.0338293
Unknown	Switzerland	1.0211730
China	Unknown	1.0137011
Brazil	Unknown	1.0059937
New Zealand	France	1.0010112
Denmark	Singapore	1.0000000
Russia	Russia	1.0000000
Israel	France	0.9998031
Spain	Norway	0.9992790
Unknown	Fiji	0.9977071
Israel	United States	0.9972796
Netherlands	New Caledonia	0.9862442
Austria	Poland	0.9847822
Denmark	Poland	0.9838720
Italy	Australia	0.9748095
Switzerland	Unknown	0.9725535
Unknown	Austria	0.9647271
France	New Caledonia	0.9581810
France	Hong Kong	0.9545580
Finland	Unknown	0.9527975
Poland	France	0.9423647
Unknown	Portugal	0.9380079
United States	Nigeria	0.9345863
Unknown	Czech Republic	0.9224215
Netherlands	China	0.9220582
Germany	Colombia	0.8995402
Germany	Italy	0.8971911
France	Germany	0.8920140
Belgium	Canada	0.8573285
Canada	Sweden	0.8527621
United States	Israel	0.8506106
Italy	Ecuador	0.8386308
Canada	Australia	0.7810893
Bulgaria	United States	0.7757486
Germany	Czech Republic	0.7691053
Denmark	Australia	0.7651278
France	Canada	0.7521236
Greece	Unknown	0.7242482
Unknown	Norway	0.7230468
Lithuania	Switzerland	0.7128352
Australia	Russia	0.7117685
United States	Lithuania	0.6849539
Germany	South Korea	0.6785448
United States	Poland	0.6559873
Germany	France	0.6452659
Sweden	Unknown	0.6360338
Unknown	Singapore	0.6316281
Bulgaria	Poland	0.6177956
Germany	Chile	0.6159934
Bulgaria	Austria	0.6148564
Australia	Spain	0.6140359
Norway	Spain	0.5967587
Norway	Canada	0.5853739
Unknown	South Korea	0.5825402
Bulgaria	France	0.5774379
France	Belgium	0.5595327
Canada	Germany	0.5285778
Unknown	Curaçao	0.5121700
Austria	Unknown	0.5114360
United States	Sweden	0.5072172
Netherlands	Peru	0.5069443
France	Ireland	0.4821894
Germany	Switzerland	0.4633026
Finland	Germany	0.4559689
Finland	Norway	0.4482860
United States	Colombia	0.4478594
United States	Norway	0.4472529
United States	New Caledonia	0.4326497
United States	Uruguay	0.4324207
Australia	Denmark	0.4310748
United States	Panama	0.4309000
Japan	United States	0.4096919
Unknown	China	0.4071286
France	Colombia	0.4012124
Norway	Australia	0.3983430
Colombia	Germany	0.3970159
France	Switzerland	0.3871114
France	Peru	0.3841060
Bulgaria	Canada	0.3784731
Switzerland	United States	0.3773445
Canada	Netherlands	0.3664586
Unknown	Saudi Arabia	0.3570105
Bulgaria	Netherlands	0.3368614
Greece	Netherlands	0.3339926
Denmark	New Zealand	0.3323449
Bulgaria	United Kingdom	0.3290682
Norway	South Korea	0.3219288
Spain	United States	0.3163808
France	Romania	0.3092703
Netherlands	Ireland	0.3083117
Sweden	Denmark	0.3068182
Australia	Israel	0.2985812
Netherlands	Lithuania	0.2962873
Canada	Italy	0.2948238
Switzerland	Italy	0.2919480
Unknown	New Caledonia	0.2887299
Canada	Colombia	0.2822796
Unknown	Uruguay	0.2717018
Unknown	Panama	0.2707463
Netherlands	Switzerland	0.2706131
Unknown	Taiwan	0.2691071
France	Mauritius	0.2640198
Switzerland	Netherlands	0.2574750
Bulgaria	China	0.2567134
Bulgaria	Taiwan	0.2526643
Sweden	Sweden	0.2498985
Denmark	Canada	0.2355911
Unknown	Mauritius	0.2304751
Unknown	Romania	0.2230253
Denmark	Italy	0.2197927
Colombia	Spain	0.2175700
Denmark	Austria	0.2122025
France	Brazil	0.2111520
Unknown	Greece	0.2090994
Denmark	Norway	0.2075330
Bulgaria	Czech Republic	0.2022632
Denmark	China	0.2005919
Australia	New Caledonia	0.1962969
Canada	Czech Republic	0.1952466
Japan	Australia	0.1880885
Denmark	Peru	0.1878818
Switzerland	Switzerland	0.1844749
Poland	Unknown	0.1843025
Canada	Norway	0.1813057
Denmark	Belgium	0.1758419
Denmark	Brazil	0.1752140
United Kingdom	Italy	0.1630648
Canada	Austria	0.1621032
Netherlands	Italy	0.1619164
Canada	China	0.1577323
Australia	France	0.1505368
France	Sweden	0.1497168
Belgium	Unknown	0.1493700
Denmark	Ireland	0.1483553
France	Australia	0.1465289
Canada	Peru	0.1457408
Japan	Germany	0.1442236
Japan	United Kingdom	0.1423299
Canada	Denmark	0.1404967
Denmark	New Caledonia	0.1374681
Denmark	Uruguay	0.1372391
Denmark	Panama	0.1367564
Denmark	South Korea	0.1365204
Unknown	Ecuador	0.1360882
Denmark	Taiwan	0.1352712
Canada	Brazil	0.1349812
Norway	United Kingdom	0.1315918
United States	Greece	0.1260161
Colombia	Greece	0.1258970
France	Italy	0.1192478
Australia	New Zealand	0.1169271
Colombia	United States	0.1139295
Canada	Ireland	0.1109200
Sweden	Germany	0.1107721
Colombia	United Kingdom	0.1096227
Netherlands	Australia	0.1086190
Canada	Taiwan	0.1079066
Canada	Poland	0.1071618
Canada	South Korea	0.1052102
Norway	Germany	0.1049158
Canada	New Caledonia	0.1047443
Canada	Uruguay	0.1047443
Canada	Panama	0.1043759
Switzerland	Austria	0.1006642
Switzerland	Norway	0.1006271
Bulgaria	Germany	0.0961149
China	United Kingdom	0.0934190
China	Australia	0.0928251
China	Romania	0.0926876
Australia	Netherlands	0.0900973
United States	Iran	0.0895339
Colombia	Switzerland	0.0866403
Lithuania	Unknown	0.0850502
Switzerland	Canada	0.0831040
Norway	Italy	0.0777531
Norway	Czech Republic	0.0776187
Norway	Israel	0.0775624
Switzerland	Germany	0.0753103
Denmark	Switzerland	0.0744288
Switzerland	Israel	0.0671474
Japan	Netherlands	0.0670224
Bulgaria	Italy	0.0664073
Unknown	Israel	0.0642599
Netherlands	United Kingdom	0.0642527
Canada	Belgium	0.0635688
Norway	Chile	0.0623432
United States	Portugal	0.0618976
Belgium	United States	0.0613799
Switzerland	Australia	0.0612307
Switzerland	Czech Republic	0.0606923
Canada	Switzerland	0.0589714
Germany	New Zealand	0.0584768
Belgium	Australia	0.0569835
Switzerland	Saudi Arabia	0.0568921
Sweden	Greece	0.0561070
Norway	France	0.0558811
France	Denmark	0.0556274
France	Iran	0.0512067
Unknown	Russia	0.0505592
Japan	Austria	0.0505173
Denmark	Czech Republic	0.0503192
Australia	Italy	0.0489262
Switzerland	Chile	0.0485827
Japan	China	0.0476934
New Zealand	Unknown	0.0469499
Japan	Peru	0.0462711
Japan	Canada	0.0448302
Switzerland	France	0.0445385
Canada	France	0.0441761
Colombia	Brazil	0.0440714
Japan	Denmark	0.0439129
Japan	Brazil	0.0425713
United States	Russia	0.0414637
United Kingdom	France	0.0409140
Japan	New Zealand	0.0388648
Italy	Unknown	0.0388300
Unknown	Iran	0.0377344
France	New Zealand	0.0372241
Japan	Italy	0.0370687
Colombia	Netherlands	0.0366625
Germany	Sweden	0.0362615
Canada	Israel	0.0358376
Bulgaria	Spain	0.0342123
Japan	Norway	0.0338562
Japan	South Korea	0.0333496
Japan	Poland	0.0333440
Japan	Uruguay	0.0326517
Japan	New Caledonia	0.0326517
Japan	Panama	0.0325369
Japan	Taiwan	0.0321835
Germany	Belgium	0.0319093
France	Argentina	0.0308185
Poland	Denmark	0.0294982
United Kingdom	Singapore	0.0294045
Bulgaria	Nigeria	0.0289694
United Kingdom	New Caledonia	0.0286144
Unknown	Croatia	0.0280926
Canada	Chile	0.0280021
Australia	Norway	0.0262178
Spain	France	0.0262091
Poland	United States	0.0252270
United Kingdom	Hong Kong	0.0249606
Spain	Spain	0.0243645
United Kingdom	Canada	0.0240402
Australia	Germany	0.0237971
Japan	Ireland	0.0237783
Netherlands	Brazil	0.0237204
Germany	Peru	0.0223014
Netherlands	Austria	0.0221971
Australia	United Kingdom	0.0214205
Japan	Belgium	0.0208931
Bulgaria	Belgium	0.0202719
Netherlands	Canada	0.0199530
New Zealand	United States	0.0198287
Netherlands	South Korea	0.0196425
Unknown	Vietnam	0.0193056
Netherlands	Denmark	0.0192819
United Kingdom	Australia	0.0189649
United Kingdom	Germany	0.0182602
Austria	Australia	0.0179254
Poland	Israel	0.0177945
France	South Korea	0.0175913
Germany	Romania	0.0167968
United States	Curaçao	0.0167415
United Kingdom	Norway	0.0167285
Unknown	Hong Kong	0.0165701
Finland	United States	0.0165472
Netherlands	Norway	0.0165328
Japan	Switzerland	0.0164776
Bulgaria	Ireland	0.0164602
Belgium	Germany	0.0161245
Bulgaria	Peru	0.0159461
Taiwan	Unknown	0.0158341
South Korea	Sweden	0.0158018
Australia	Czech Republic	0.0157652
New Zealand	Australia	0.0152377
Poland	Poland	0.0151597
Poland	Austria	0.0151359
Austria	United Kingdom	0.0151351
Italy	Spain	0.0148336
Germany	Mauritius	0.0146504
Netherlands	Poland	0.0146380
Italy	Colombia	0.0143472
Netherlands	New Zealand	0.0143121
Netherlands	Uruguay	0.0143048
Netherlands	Panama	0.0142545
Netherlands	Taiwan	0.0142112
United Kingdom	Ireland	0.0142092
Netherlands	Belgium	0.0139892
United States	South Africa	0.0134590
Germany	Saudi Arabia	0.0133996
Poland	Netherlands	0.0129269
Germany	Poland	0.0128339
Taiwan	United States	0.0125941
Spain	Poland	0.0125781
Australia	Chile	0.0125428
Taiwan	Australia	0.0119497
United States	Hungary	0.0119127
Greece	United States	0.0117563
Denmark	France	0.0117198
Colombia	Australia	0.0115513
Japan	Czech Republic	0.0114539
Chile	Unknown	0.0114306
Greece	Germany	0.0113962
Poland	Czech Republic	0.0112703
Unknown	Chile	0.0110268
United Kingdom	Colombia	0.0104980
Bulgaria	Australia	0.0104409
United Kingdom	Czech Republic	0.0102863
United Kingdom	Israel	0.0102854
Bulgaria	New Zealand	0.0102791
Poland	Canada	0.0101044
Colombia	New Zealand	0.0100556
Spain	United Kingdom	0.0096010
Australia	Nigeria	0.0092871
United States	Saudi Arabia	0.0092137
Colombia	Denmark	0.0090860
Bulgaria	Lithuania	0.0090474
Germany	Brazil	0.0087518
United Kingdom	Spain	0.0082342
Poland	United Kingdom	0.0081089
Spain	Brazil	0.0080278
Bulgaria	Switzerland	0.0079930
Unknown	Nigeria	0.0079273
United Kingdom	Chile	0.0078933
Bulgaria	Romania	0.0073016
Bulgaria	Mauritius	0.0069345
Unknown	South Africa	0.0069098
Unknown	Finland	0.0067922
Spain	South Korea	0.0066939
France	Poland	0.0066835
Finland	Belgium	0.0065901
Poland	Italy	0.0064869
Norway	Saudi Arabia	0.0063399
Ukraine	Unknown	0.0063074
Singapore	Australia	0.0060268
France	Austria	0.0058384
France	Norway	0.0056087
Denmark	Nigeria	0.0055300
United States	Chile	0.0055177
Denmark	Israel	0.0055002
Germany	Denmark	0.0054771
Spain	Germany	0.0053937
Austria	France	0.0052599
New Caledonia	Unknown	0.0052364
Netherlands	Czech Republic	0.0052323
Italy	United States	0.0051895
Denmark	Spain	0.0051378
France	China	0.0051080
France	Israel	0.0048205
Portugal	Unknown	0.0046360
Spain	Canada	0.0045777
Finland	Netherlands	0.0045139
Finland	Peru	0.0045076
Denmark	Chile	0.0042128
Colombia	France	0.0041863
Colombia	Sweden	0.0040938
Singapore	Unknown	0.0040324
Chile	United States	0.0040093
Ukraine	Italy	0.0039478
Germany	Ecuador	0.0038602
Switzerland	Ecuador	0.0038602
Austria	Netherlands	0.0037632
United Kingdom	Nigeria	0.0036867
Poland	Norway	0.0036790
Switzerland	Spain	0.0036213
Austria	New Caledonia	0.0036085
Austria	Hong Kong	0.0036029
Japan	France	0.0035910
Poland	Germany	0.0035711
Denmark	Sweden	0.0035000
Spain	Switzerland	0.0034732
France	Uruguay	0.0033940
Uganda	Unknown	0.0033897
France	Panama	0.0033821
France	Taiwan	0.0033638
United States	Croatia	0.0033457
Italy	Norway	0.0033121
United Kingdom	South Korea	0.0032662
Sweden	Australia	0.0032072
United Kingdom	Brazil	0.0031792
Brazil	United States	0.0031430
United Kingdom	New Zealand	0.0031081
Finland	Spain	0.0031065
Finland	Romania	0.0031065
Spain	Australia	0.0030526
United Arab Emirates	Unknown	0.0030249
Belgium	Brazil	0.0030008
Spain	New Zealand	0.0029926
Finland	United Kingdom	0.0029745
Unknown	Lithuania	0.0029621
Finland	Mauritius	0.0029502
Mexico	Unknown	0.0029216
Japan	Spain	0.0028533
Argentina	Unknown	0.0028179
Germany	Iran	0.0028132
Canada	Nigeria	0.0027801
Belgium	South Korea	0.0027615
Austria	Canada	0.0027188
Canada	Spain	0.0026913
Kenya	Unknown	0.0026790
New Zealand	Germany	0.0026715
France	Czech Republic	0.0026421
Netherlands	France	0.0025869
China	United States	0.0025423
United Kingdom	Denmark	0.0025162
United States	Singapore	0.0025121
Finland	France	0.0024718
United Kingdom	Sweden	0.0024510
Poland	Australia	0.0024258
New Zealand	Spain	0.0024141
China	Spain	0.0023455
Unknown	Japan	0.0023344
New Zealand	United Kingdom	0.0023064
Ukraine	United States	0.0022998
Spain	Ireland	0.0022905
Spain	Netherlands	0.0022856
Portugal	Belgium	0.0022806
Belgium	Peru	0.0022471
Colombia	Poland	0.0021876
Netherlands	Spain	0.0021545
Italy	Germany	0.0020929
New Zealand	Ireland	0.0020238
Canada	Curaçao	0.0020178
Portugal	United States	0.0020133
Netherlands	Romania	0.0020044
Switzerland	Belgium	0.0019900
Greenland	Unknown	0.0019842
Spain	Belgium	0.0019662
Antarctica	Unknown	0.0018835
Norway	Brazil	0.0018735
New Zealand	Lithuania	0.0018420
Netherlands	Mauritius	0.0018407
Finland	Denmark	0.0018355
Chile	Australia	0.0018312
South Africa	Unknown	0.0018268
Brazil	New Zealand	0.0018180
Austria	Ireland	0.0017710
Poland	Chile	0.0017650
Sweden	Netherlands	0.0017490
Japan	Nigeria	0.0017124
Italy	Israel	0.0016718
Sweden	United Kingdom	0.0016704
Denmark	Croatia	0.0016311
Antarctica	United States	0.0016147
Belgium	New Caledonia	0.0016039
India	Mauritius	0.0015996
Bulgaria	Brazil	0.0015963
Uruguay	Canada	0.0015718
Germany	Austria	0.0015611
Sweden	Spain	0.0015609
Spain	China	0.0015585
Sweden	France	0.0015392
Australia	Switzerland	0.0015271
United Kingdom	Austria	0.0015167
Austria	Colombia	0.0015143
Portugal	Netherlands	0.0015132
Spain	Taiwan	0.0015128
United Arab Emirates	United States	0.0015084
United Arab Emirates	Australia	0.0014849
Bulgaria	Sweden	0.0014771
Austria	Germany	0.0014466
Sweden	Ireland	0.0014365
Chile	United Kingdom	0.0014351
Belgium	Spain	0.0014311
Chile	Germany	0.0014294
Brazil	United Kingdom	0.0014177
Sweden	Saudi Arabia	0.0014128
Germany	Ireland	0.0014026
China	Germany	0.0013954
Finland	Australia	0.0013888
Spain	Peru	0.0013796
United Kingdom	Poland	0.0013763
Bulgaria	Iran	0.0013449
Bulgaria	South Korea	0.0013220
United Kingdom	Peru	0.0013107
China	Denmark	0.0012872
Finland	Canada	0.0012629
New Zealand	New Zealand	0.0012489
Australia	Peru	0.0012300
Colombia	Belgium	0.0012251
Austria	Belgium	0.0012134
Sweden	Switzerland	0.0011991
Japan	Romania	0.0011935
Uganda	United States	0.0011899
New Zealand	Italy	0.0011746
Singapore	Germany	0.0011616
Japan	Israel	0.0011439
Italy	Czech Republic	0.0011382
Germany	Lithuania	0.0011377
Japan	Mauritius	0.0011335
New Zealand	Norway	0.0010773
France	Chile	0.0010677
Brazil	Australia	0.0010652
China	Switzerland	0.0010574
China	Netherlands	0.0010487
Ukraine	Germany	0.0010473
Brazil	Germany	0.0010455
New Zealand	Switzerland	0.0010339
United Kingdom	Switzerland	0.0010188
Japan	Sweden	0.0010038
India	Unknown	0.0009953
Italy	France	0.0009951
Mexico	United States	0.0009933
Turkey	Unknown	0.0009694
Finland	Austria	0.0009617
Argentina	United States	0.0009523
Canada	South Africa	0.0009517
Brazil	France	0.0009503
Brazil	Ireland	0.0009488
Kenya	United States	0.0009271
Ukraine	Australia	0.0009261
Finland	Poland	0.0009261
Belgium	United Kingdom	0.0009257
New Zealand	Peru	0.0009190
China	France	0.0009143
Taiwan	Spain	0.0009137
Italy	Chile	0.0009114
India	United States	0.0008787
Italy	Brazil	0.0008740
New Zealand	Netherlands	0.0008702
Colombia	Peru	0.0008680
Norway	Netherlands	0.0008580
Norway	Peru	0.0008514
Germany	Mexico	0.0008489
United Kingdom	China	0.0008407
Colombia	Canada	0.0008375
Austria	Peru	0.0008286
Bulgaria	Norway	0.0008100
Bulgaria	New Caledonia	0.0008099
Bulgaria	Uruguay	0.0008099
Bulgaria	Panama	0.0008070
United Kingdom	Argentina	0.0008059
New Zealand	Brazil	0.0007711
United Kingdom	Russia	0.0007700
Germany	New Caledonia	0.0007478
Australia	Brazil	0.0007444
Ukraine	United Kingdom	0.0007331
New Zealand	Israel	0.0007185
Greenland	United States	0.0006965
Italy	South Korea	0.0006902
New Zealand	Canada	0.0006897
Czech Republic	Unknown	0.0006842
Chile	Netherlands	0.0006695
Canada	Romania	0.0006688
Colombia	Romania	0.0006599
Netherlands	Sweden	0.0006440
United Kingdom	Taiwan	0.0006393
Sweden	Russia	0.0006311
Switzerland	Nigeria	0.0006245
China	Poland	0.0006180
Switzerland	Brazil	0.0006172
China	Austria	0.0006165
Italy	Switzerland	0.0006101
France	Curaçao	0.0006048
Austria	Spain	0.0006045
Austria	Romania	0.0006045
China	Canada	0.0005998
China	Italy	0.0005960
New Zealand	Czech Republic	0.0005925
United Kingdom	Belgium	0.0005866
Japan	Chile	0.0005787
Austria	Mauritius	0.0005741
Finland	Iran	0.0005722
Antarctica	France	0.0005699
Israel	Sweden	0.0005644
New Caledonia	United States	0.0005570
France	Saudi Arabia	0.0005523
Uganda	Australia	0.0005465
Unknown	Argentina	0.0005420
Mexico	Australia	0.0005396
Netherlands	Israel	0.0005282
Uzbekistan	Unknown	0.0005236
Italy	United Kingdom	0.0005235
Antarctica	Germany	0.0005186
Antarctica	Ireland	0.0005173
Chile	Austria	0.0005113
Spain	Romania	0.0005106
Antarctica	Australia	0.0005023
Belgium	Switzerland	0.0004956
United States	Vietnam	0.0004873
Spain	Mauritius	0.0004849
Chile	China	0.0004830
China	Brazil	0.0004818
Australia	Canada	0.0004706
Chile	Peru	0.0004558
Chile	Canada	0.0004524
Finland	Sweden	0.0004471
Chile	Denmark	0.0004428
Austria	Sweden	0.0004346
New Zealand	Denmark	0.0004333
Argentina	Australia	0.0004301
Australia	Austria	0.0004301
Netherlands	South Africa	0.0004290
Uganda	United Kingdom	0.0004276
Kenya	Australia	0.0004257
Brazil	Switzerland	0.0004257
Australia	Singapore	0.0004242
Germany	China	0.0004241
Netherlands	Chile	0.0004224
Chile	Brazil	0.0004217
Uganda	Germany	0.0004207
Sweden	Belgium	0.0004170
Kenya	Italy	0.0004158
Australia	Belgium	0.0004111
Australia	China	0.0004104
New Zealand	Chile	0.0004022
Norway	Mexico	0.0004020
Switzerland	Peru	0.0003968
Greece	Italy	0.0003939
Mexico	Germany	0.0003866
United Kingdom	Uruguay	0.0003857
New Zealand	Austria	0.0003849
United Kingdom	Panama	0.0003843
Denmark	Colombia	0.0003788
Hungary	Unknown	0.0003641
Mexico	Mauritius	0.0003635
Taiwan	Switzerland	0.0003634
Mexico	United Kingdom	0.0003591
New Zealand	China	0.0003576
Netherlands	Iran	0.0003570
Spain	Denmark	0.0003525
Canada	Lithuania	0.0003433
Chile	Poland	0.0003373
Argentina	United Kingdom	0.0003362
Argentina	Germany	0.0003359
Chile	Norway	0.0003308
Chile	New Zealand	0.0003307
Chile	New Caledonia	0.0003307
Chile	Uruguay	0.0003307
Greece	France	0.0003306
Kenya	United Kingdom	0.0003302
Chile	Panama	0.0003295
United States	Bulgaria	0.0003287
Kenya	Germany	0.0003286
Chile	South Korea	0.0003284
Chile	Italy	0.0003263
Sweden	Brazil	0.0003262
Chile	Taiwan	0.0003259
Ukraine	Netherlands	0.0003244
Colombia	Ireland	0.0003226
Greenland	Australia	0.0003199
China	New Caledonia	0.0003148
Sweden	Peru	0.0003111
China	Hong Kong	0.0003084
Singapore	Italy	0.0003035
Germany	Russia	0.0003016
Colombia	Mauritius	0.0002995
Finland	Czech Republic	0.0002985
Belgium	New Zealand	0.0002953
Australia	Poland	0.0002931
Germany	Uruguay	0.0002898
Ukraine	Peru	0.0002887
Germany	Panama	0.0002887
Australia	Taiwan	0.0002886
Germany	Taiwan	0.0002857
Colombia	Austria	0.0002794
Czech Republic	United States	0.0002765
France	Greece	0.0002752
Norway	Austria	0.0002726
Colombia	Italy	0.0002699
South Korea	Unknown	0.0002657
New Zealand	Romania	0.0002650
Switzerland	New Zealand	0.0002595
New Zealand	Poland	0.0002587
Australia	Uruguay	0.0002552
Norway	China	0.0002544
Australia	Panama	0.0002543
Australia	South Korea	0.0002535
Unknown	Hungary	0.0002507
Greenland	United Kingdom	0.0002503
Australia	Bulgaria	0.0002501
Brazil	Italy	0.0002499
China	Ireland	0.0002488
Ukraine	Austria	0.0002488
Colombia	China	0.0002466
Greenland	Germany	0.0002463
New Zealand	New Caledonia	0.0002448
New Zealand	Uruguay	0.0002448
New Zealand	Panama	0.0002439
New Zealand	South Korea	0.0002431
New Zealand	Taiwan	0.0002413
Chile	Ireland	0.0002383
Norway	Denmark	0.0002368
Italy	Netherlands	0.0002359
Bulgaria	Curaçao	0.0002340
Switzerland	Denmark	0.0002328
Ukraine	China	0.0002322
Belgium	Italy	0.0002315
Antarctica	Switzerland	0.0002276
Colombia	Curaçao	0.0002251
Norway	Ireland	0.0002247
Belgium	France	0.0002243
Japan	Iran	0.0002198
Ukraine	Canada	0.0002178
Ukraine	Denmark	0.0002168
Lithuania	Germany	0.0002166
Uruguay	Germany	0.0002104
South Korea	Italy	0.0002093
Taiwan	Germany	0.0002048
Belgium	Netherlands	0.0002040
Ukraine	Brazil	0.0002026
China	Czech Republic	0.0002016
Kosovo	Germany	0.0002013
Sweden	Romania	0.0002003
Uganda	Netherlands	0.0001980
Chile	Belgium	0.0001971
Spain	Sweden	0.0001940
Portugal	Germany	0.0001919
Peru	United Kingdom	0.0001906
Sweden	Mauritius	0.0001903
Taiwan	Brazil	0.0001844
Australia	Ireland	0.0001842
Uzbekistan	United States	0.0001838
Hungary	United States	0.0001833
Colombia	South Korea	0.0001818
Norway	Poland	0.0001803
Sweden	Israel	0.0001802
United States	Ecuador	0.0001800
Unknown	India	0.0001791
United States	Finland	0.0001751
Colombia	Norway	0.0001746
Norway	New Zealand	0.0001742
Norway	New Caledonia	0.0001742
Norway	Uruguay	0.0001742
Hungary	Australia	0.0001739
Norway	Panama	0.0001736
United Kingdom	Romania	0.0001729
Uruguay	Unknown	0.0001728
Norway	Taiwan	0.0001717
Italy	Sweden	0.0001707
Finland	Curaçao	0.0001692
Colombia	New Caledonia	0.0001686
Colombia	Uruguay	0.0001686
Colombia	Panama	0.0001680
Colombia	Taiwan	0.0001661
Czech Republic	Norway	0.0001657
Chile	Switzerland	0.0001654
Luxembourg	Unknown	0.0001654
United Kingdom	Mauritius	0.0001642
Mexico	Netherlands	0.0001642
Colombia	Lithuania	0.0001625
Ukraine	Poland	0.0001623
Germany	Nigeria	0.0001612
Ukraine	Norway	0.0001590
Ukraine	New Zealand	0.0001589
Ukraine	New Caledonia	0.0001589
Ukraine	Uruguay	0.0001589
Ukraine	Panama	0.0001584
Ukraine	South Korea	0.0001578
Argentina	Netherlands	0.0001569
Ukraine	Taiwan	0.0001566
Greece	Norway	0.0001557
New Zealand	Belgium	0.0001537
Kenya	Netherlands	0.0001529
Uganda	Austria	0.0001526
Singapore	France	0.0001518
Belgium	China	0.0001514
Singapore	Nigeria	0.0001511
Taiwan	Netherlands	0.0001504
Belgium	Taiwan	0.0001489
Mexico	Peru	0.0001473
Belgium	Poland	0.0001472
Finland	China	0.0001468
Uganda	China	0.0001442
Switzerland	South Korea	0.0001428
Lithuania	United States	0.0001422
Nigeria	Italy	0.0001412
Czech Republic	Germany	0.0001405
Singapore	Spain	0.0001403
United States	Turkey	0.0001395
Norway	Belgium	0.0001390
Ukraine	Belgium	0.0001387
Netherlands	Curaçao	0.0001378
Latvia	Unknown	0.0001378
Uganda	Canada	0.0001347
Switzerland	China	0.0001340
Uganda	Peru	0.0001340
Belgium	Sweden	0.0001336
Mexico	Italy	0.0001331
Hong Kong	Unknown	0.0001330
Uganda	Denmark	0.0001321
Norway	Switzerland	0.0001316
Czech Republic	Australia	0.0001300
Finland	Brazil	0.0001300
China	Colombia	0.0001296
Mexico	Austria	0.0001266
Uganda	Brazil	0.0001259
Switzerland	Sweden	0.0001248
Argentina	Austria	0.0001236
Australia	Romania	0.0001225
Germany	South Africa	0.0001198
Mexico	China	0.0001196
Denmark	South Africa	0.0001190
Kenya	Austria	0.0001182
Australia	Mauritius	0.0001163
Austria	Argentina	0.0001163
Greenland	Netherlands	0.0001159
Ukraine	Ireland	0.0001148
China	Belgium	0.0001133
Turkey	China	0.0001127
Argentina	China	0.0001126
Argentina	Denmark	0.0001119
Mexico	Canada	0.0001117
Kenya	China	0.0001114
Austria	Iran	0.0001113
Turkey	Taiwan	0.0001111
Mexico	Denmark	0.0001096
Chile	Czech Republic	0.0001086
Bulgaria	Bulgaria	0.0001084
Argentina	Canada	0.0001079
Finland	Italy	0.0001050
Argentina	Peru	0.0001046
Mexico	Brazil	0.0001044
Switzerland	Romania	0.0001042
Kenya	Canada	0.0001041
Kenya	Peru	0.0001035
Poland	China	0.0001028
Greece	Czech Republic	0.0001022
Kenya	Denmark	0.0001020
Czech Republic	Italy	0.0001015
Uganda	Poland	0.0001007
Finland	New Zealand	0.0001003
Finland	Uruguay	0.0001003
Finland	New Caledonia	0.0001003
Finland	Panama	0.0001000
Finland	South Korea	0.0000996
Unknown	Bulgaria	0.0000995
Switzerland	Mauritius	0.0000989
Finland	Taiwan	0.0000989
Czech Republic	Czech Republic	0.0000989
Uganda	Norway	0.0000987
Uganda	New Zealand	0.0000987
Uganda	New Caledonia	0.0000987
Uganda	Uruguay	0.0000987
Uganda	Panama	0.0000984
Argentina	Brazil	0.0000983
Uganda	South Korea	0.0000981
Czech Republic	Israel	0.0000976
Uganda	Italy	0.0000974
Uganda	Taiwan	0.0000973
Kenya	Brazil	0.0000972
Switzerland	Taiwan	0.0000971
Portugal	Peru	0.0000955
Spain	Iran	0.0000940
Canada	Mauritius	0.0000940
Greece	Australia	0.0000939
Greece	Israel	0.0000939
Niue	Australia	0.0000918
Italy	Poland	0.0000917
Italy	Portugal	0.0000917
France	Bulgaria	0.0000915
Australia	South Africa	0.0000907
Denmark	Lithuania	0.0000905
Japan	Colombia	0.0000901
Switzerland	Poland	0.0000900
Greenland	Austria	0.0000894
Singapore	Colombia	0.0000883
Sweden	Canada	0.0000878
Unknown	Turkey	0.0000877
South Africa	United States	0.0000871
Italy	Denmark	0.0000865
Uzbekistan	Australia	0.0000844
Greenland	China	0.0000844
Mexico	Poland	0.0000835
Argentina	Poland	0.0000831
Argentina	Norway	0.0000831
Kenya	Norway	0.0000823
Ukraine	Switzerland	0.0000820
Mexico	Norway	0.0000819
Mexico	New Zealand	0.0000819
Mexico	New Caledonia	0.0000819
Mexico	Uruguay	0.0000819
Nepal	United States	0.0000819
Mexico	Panama	0.0000816
Mexico	South Korea	0.0000813
Mexico	Taiwan	0.0000807
Belgium	Nigeria	0.0000806
Spain	Austria	0.0000805
Argentina	Italy	0.0000796
Poland	Romania	0.0000795
Bulgaria	Croatia	0.0000792
Greenland	Canada	0.0000789
Greenland	Peru	0.0000784
Czech Republic	Chile	0.0000781
Russia	Unknown	0.0000780
Kenya	Poland	0.0000778
Greenland	Denmark	0.0000773
Argentina	New Zealand	0.0000771
Argentina	New Caledonia	0.0000771
Argentina	Uruguay	0.0000771
Argentina	Panama	0.0000768
Argentina	South Korea	0.0000765
Kenya	New Zealand	0.0000763
Kenya	New Caledonia	0.0000763
Kenya	Uruguay	0.0000763
Kenya	Panama	0.0000760
Argentina	Taiwan	0.0000759
Kenya	South Korea	0.0000757
Greece	Chile	0.0000752
Kenya	Taiwan	0.0000752
Switzerland	Ireland	0.0000747
China	Sweden	0.0000743
Greenland	Brazil	0.0000737
Finland	Ireland	0.0000723
Egypt	Unknown	0.0000723
France	Lithuania	0.0000722
Belgium	Denmark	0.0000715
Uganda	Ireland	0.0000711
Finland	Switzerland	0.0000708
Portugal	Spain	0.0000704
Portugal	Romania	0.0000704
Czech Republic	France	0.0000702
Brazil	Netherlands	0.0000676
Portugal	Mauritius	0.0000669
Uzbekistan	United Kingdom	0.0000660
Colombia	Czech Republic	0.0000651
Uzbekistan	Germany	0.0000650
Hong Kong	United States	0.0000642
Australia	Sweden	0.0000626
Israel	Israel	0.0000616
Switzerland	Uruguay	0.0000610
Switzerland	New Caledonia	0.0000610
Switzerland	Panama	0.0000608
United States	Japan	0.0000597
Mexico	Ireland	0.0000590
Greenland	Poland	0.0000589
Colombia	Iran	0.0000581
Belgium	Ireland	0.0000581
Luxembourg	United States	0.0000580
Greenland	Norway	0.0000578
Greenland	New Zealand	0.0000578
Greenland	New Caledonia	0.0000578
Greenland	Uruguay	0.0000578
Greenland	Panama	0.0000576
Greenland	South Korea	0.0000574
Greenland	Italy	0.0000570
Greenland	Taiwan	0.0000570
Nepal	Unknown	0.0000567
Uganda	Belgium	0.0000558
Argentina	Ireland	0.0000555
Sweden	Poland	0.0000553
Kenya	Ireland	0.0000549
United Kingdom	Lithuania	0.0000542
Ukraine	Czech Republic	0.0000522
Spain	New Caledonia	0.0000514
Spain	Uruguay	0.0000514
Spain	Panama	0.0000512
Uganda	Switzerland	0.0000494
Brazil	Canada	0.0000492
Belgium	Norway	0.0000487
New Zealand	Sweden	0.0000486
Latvia	United States	0.0000484
Hong Kong	Norway	0.0000479
Portugal	United Kingdom	0.0000479
China	New Zealand	0.0000475
Mexico	Belgium	0.0000462
Nigeria	Unknown	0.0000460
Ireland	Unknown	0.0000457
Israel	Australia	0.0000454
Netherlands	Colombia	0.0000454
Niue	Unknown	0.0000449
Sweden	Austria	0.0000447
Denmark	Turkey	0.0000443
Finland	Finland	0.0000438
Argentina	Belgium	0.0000435
Kenya	Belgium	0.0000431
Portugal	Vietnam	0.0000426
Sweden	China	0.0000422
Antarctica	Poland	0.0000420
Argentina	Switzerland	0.0000420
Greenland	Ireland	0.0000416
Brazil	Brazil	0.0000414
Mexico	Switzerland	0.0000409
Portugal	France	0.0000409
Netherlands	Nigeria	0.0000403
Costa Rica	Unknown	0.0000398
South Africa	Germany	0.0000392
Brazil	Poland	0.0000389
Kenya	Switzerland	0.0000381
Taiwan	Denmark	0.0000371
Sweden	Iran	0.0000369
Japan	Lithuania	0.0000362
China	Lithuania	0.0000361
Italy	Russia	0.0000357
Niue	Russia	0.0000357
Egypt	United States	0.0000357
Netherlands	Vietnam	0.0000353
Poland	Peru	0.0000349
Portugal	Australia	0.0000342
Russia	Israel	0.0000341
Canada	Turkey	0.0000338
Austria	Denmark	0.0000337
Finland	Singapore	0.0000336
Germany	Curaçao	0.0000334
Switzerland	Vietnam	0.0000331
Poland	Brazil	0.0000328
Greenland	Belgium	0.0000326
Uganda	Czech Republic	0.0000324
Belgium	Czech Republic	0.0000324
United Kingdom	Iran	0.0000318
Bulgaria	South Africa	0.0000317
Brazil	Austria	0.0000312
Uzbekistan	Netherlands	0.0000306
Argentina	Czech Republic	0.0000304
Antarctica	New Zealand	0.0000303
Colombia	Nigeria	0.0000302
New Zealand	Nigeria	0.0000302
Spain	Nigeria	0.0000302
Singapore	United States	0.0000299
Brazil	Denmark	0.0000298
Brazil	China	0.0000293
Brazil	South Korea	0.0000291
Hong Kong	Germany	0.0000290
Belgium	Israel	0.0000289
Hong Kong	Australia	0.0000289
Hong Kong	Czech Republic	0.0000289
Hong Kong	Israel	0.0000289
Hong Kong	Italy	0.0000289
Sweden	Norway	0.0000289
Greenland	Switzerland	0.0000289
Sweden	New Zealand	0.0000289
Sweden	New Caledonia	0.0000289
Sweden	Uruguay	0.0000289
Sweden	Italy	0.0000288
Sweden	Panama	0.0000288
Brazil	Peru	0.0000288
Sweden	South Korea	0.0000287
Kenya	Czech Republic	0.0000286
Sweden	Taiwan	0.0000285
South Africa	United Kingdom	0.0000279
Iceland	Unknown	0.0000276
Poland	South Africa	0.0000269
Mexico	Czech Republic	0.0000269
Luxembourg	Australia	0.0000267
Brazil	Norway	0.0000261
South Africa	Belgium	0.0000260
Poland	New Zealand	0.0000257
Poland	New Caledonia	0.0000257
Poland	Uruguay	0.0000257
Spain	Greece	0.0000257
Poland	Panama	0.0000256
Poland	South Korea	0.0000255
Poland	Taiwan	0.0000253
Israel	United Kingdom	0.0000251
Ukraine	South Africa	0.0000240
Unknown	Luxembourg	0.0000236
Uzbekistan	Austria	0.0000236
Belgium	Chile	0.0000231
Hong Kong	Chile	0.0000231
India	Denmark	0.0000231
South Africa	Italy	0.0000226
Australia	Iran	0.0000226
Uzbekistan	China	0.0000223
Latvia	Australia	0.0000222
Cyprus	Unknown	0.0000222
Germany	Vietnam	0.0000217
South Africa	Spain	0.0000217
Australia	Fiji	0.0000212
Ukraine	Curaçao	0.0000212
Spain	Czech Republic	0.0000209
Luxembourg	United Kingdom	0.0000209
Colombia	Bulgaria	0.0000208
Uzbekistan	Canada	0.0000208
Hong Kong	France	0.0000208
Uzbekistan	Peru	0.0000207
South Africa	Netherlands	0.0000206
Ireland	Italy	0.0000206
Luxembourg	Germany	0.0000205
Uzbekistan	Denmark	0.0000204
Israel	Denmark	0.0000203
Norway	Romania	0.0000203
Brazil	New Caledonia	0.0000201
Brazil	Uruguay	0.0000201
Brazil	Panama	0.0000200
South Africa	Peru	0.0000200
Russia	Germany	0.0000199
Brazil	Taiwan	0.0000198
Uzbekistan	Brazil	0.0000194
Antarctica	Canada	0.0000194
Egypt	Germany	0.0000194
Norway	Mauritius	0.0000193
Switzerland	Iran	0.0000192
Antarctica	United Kingdom	0.0000190
Greenland	Czech Republic	0.0000190
Poland	Ireland	0.0000185
Canada	Iran	0.0000182
Brazil	Spain	0.0000181
Ghana	Germany	0.0000181
Ghana	Peru	0.0000181
Czech Republic	United Kingdom	0.0000174
Latvia	United Kingdom	0.0000174
Antarctica	Netherlands	0.0000172
Italy	New Zealand	0.0000171
Latvia	Germany	0.0000171
South Africa	France	0.0000171
Taiwan	Sweden	0.0000170
Cyprus	Germany	0.0000161
Portugal	Canada	0.0000161
Mexico	Russia	0.0000159
Uzbekistan	Poland	0.0000156
Uzbekistan	Norway	0.0000153
Uzbekistan	New Zealand	0.0000153
Uzbekistan	New Caledonia	0.0000153
Uzbekistan	Uruguay	0.0000153
Antarctica	Belgium	0.0000152
Uzbekistan	Panama	0.0000152
South Africa	Australia	0.0000152
China	Peru	0.0000152
Uzbekistan	South Korea	0.0000151
Spain	Croatia	0.0000150
Uzbekistan	Italy	0.0000150
Uzbekistan	Taiwan	0.0000150
Argentina	Israel	0.0000150
Portugal	Finland	0.0000149
Poland	Belgium	0.0000145
Czech Republic	New Zealand	0.0000143
New Caledonia	United Kingdom	0.0000138
Brazil	Belgium	0.0000136
Poland	Switzerland	0.0000131
Egypt	United Kingdom	0.0000131
Portugal	Iran	0.0000130
Switzerland	Curaçao	0.0000130
Japan	Curaçao	0.0000130
Netherlands	Finland	0.0000126
United Kingdom	South Africa	0.0000123
South Africa	Romania	0.0000123
Bangladesh	Germany	0.0000120
South Africa	Mauritius	0.0000117
India	Poland	0.0000117
Germany	Finland	0.0000117
India	Austria	0.0000116
Peru	Unknown	0.0000116
Switzerland	Finland	0.0000116
Peru	Israel	0.0000114
United States	Argentina	0.0000112
Belgium	Austria	0.0000111
Greece	Canada	0.0000111
Israel	Austria	0.0000111
Uzbekistan	Ireland	0.0000110
Israel	Germany	0.0000110
Greece	Switzerland	0.0000109
Cyprus	Switzerland	0.0000109
Poland	Sweden	0.0000109
Czech Republic	Sweden	0.0000108
Costa Rica	Germany	0.0000108
India	United Kingdom	0.0000107
Israel	Poland	0.0000107
Japan	Turkey	0.0000105
Australia	Curaçao	0.0000102
India	France	0.0000102
Brazil	Czech Republic	0.0000102
Niue	Israel	0.0000101
Ireland	France	0.0000101
Ireland	Nigeria	0.0000101
Norway	Nigeria	0.0000101
South Africa	Nigeria	0.0000101
Portugal	Sweden	0.0000100
China	Argentina	0.0000100
India	Spain	0.0000099
Nigeria	United States	0.0000097
Luxembourg	Netherlands	0.0000097
Austria	Austria	0.0000096
Sweden	Czech Republic	0.0000095
China	China	0.0000095
Ireland	Spain	0.0000094
Chile	Sweden	0.0000092
India	Germany	0.0000091
Spain	Lithuania	0.0000090
Egypt	Australia	0.0000089
Singapore	Norway	0.0000088
Uzbekistan	Belgium	0.0000086
Ireland	United States	0.0000083
Greece	Russia	0.0000082
Israel	Netherlands	0.0000082
Austria	China	0.0000082
New Zealand	Curaçao	0.0000082
Costa Rica	Norway	0.0000081
Czech Republic	Netherlands	0.0000080
Latvia	Netherlands	0.0000080
Spain	South Africa	0.0000080
France	Croatia	0.0000080
Brazil	Russia	0.0000079
New Zealand	Russia	0.0000078
India	Netherlands	0.0000078
Argentina	France	0.0000076
Uzbekistan	Switzerland	0.0000076
Antarctica	Italy	0.0000076
Luxembourg	Austria	0.0000074
Denmark	Finland	0.0000074
Norway	Sweden	0.0000073
Israel	Canada	0.0000073
Slovenia	Unknown	0.0000073
Chile	France	0.0000072
Austria	Brazil	0.0000072
India	Canada	0.0000071
Luxembourg	China	0.0000070
Portugal	Switzerland	0.0000070
Costa Rica	Australia	0.0000070
Costa Rica	New Zealand	0.0000068
Singapore	New Zealand	0.0000068
Canada	Croatia	0.0000068
Uruguay	Switzerland	0.0000067
Netherlands	Greece	0.0000067
Colombia	Israel	0.0000066
Luxembourg	Canada	0.0000066
Luxembourg	Peru	0.0000065
Austria	Italy	0.0000065
Taiwan	United Kingdom	0.0000065
Brazil	Israel	0.0000065
Luxembourg	Denmark	0.0000064
China	Norway	0.0000064
China	Uruguay	0.0000064
China	Panama	0.0000064
China	South Korea	0.0000064
China	Taiwan	0.0000063
Brazil	Sweden	0.0000063
Egypt	Peru	0.0000063
Czech Republic	Austria	0.0000062
Latvia	Austria	0.0000062
New Zealand	Greece	0.0000062
Luxembourg	Brazil	0.0000061
Singapore	Denmark	0.0000061
Czech Republic	China	0.0000059
Latvia	China	0.0000059
South Africa	Canada	0.0000058
Austria	Norway	0.0000056
Austria	New Zealand	0.0000056
Austria	Uruguay	0.0000056
Austria	Panama	0.0000056
Austria	South Korea	0.0000056
Austria	Taiwan	0.0000055
Czech Republic	Canada	0.0000055
Latvia	Canada	0.0000055
Czech Republic	Peru	0.0000054
Latvia	Peru	0.0000054
Czech Republic	Denmark	0.0000054
Latvia	Denmark	0.0000054
Antarctica	Brazil	0.0000053
Costa Rica	United States	0.0000052
Portugal	Japan	0.0000052
Chile	Spain	0.0000052
Chile	Romania	0.0000052
Czech Republic	Brazil	0.0000051
Latvia	Brazil	0.0000051
New Zealand	Fiji	0.0000050
Uzbekistan	Czech Republic	0.0000050
Luxembourg	Poland	0.0000049
Chile	Mauritius	0.0000049
Israel	Norway	0.0000048
Luxembourg	Norway	0.0000048
Luxembourg	New Zealand	0.0000048
Luxembourg	New Caledonia	0.0000048
Luxembourg	Uruguay	0.0000048
Spain	Israel	0.0000048
Luxembourg	Panama	0.0000048
Luxembourg	South Korea	0.0000048
Spain	Curaçao	0.0000048
Luxembourg	Italy	0.0000048
Luxembourg	Taiwan	0.0000047
Ukraine	Sweden	0.0000047
United Kingdom	Saudi Arabia	0.0000047
Ukraine	France	0.0000047
Taiwan	Greece	0.0000046
Netherlands	Turkey	0.0000046
Ukraine	Spain	0.0000046
Ukraine	Romania	0.0000046
Mexico	Israel	0.0000045
Unknown	Tunisia	0.0000045
Nigeria	Australia	0.0000044
Ukraine	Mauritius	0.0000043
Netherlands	Japan	0.0000043
United States	India	0.0000042
South Africa	Denmark	0.0000041
Portugal	Greece	0.0000041
Czech Republic	Poland	0.0000041
Latvia	Poland	0.0000041
Nepal	Germany	0.0000040
United States	Fiji	0.0000040
Latvia	Norway	0.0000040
Latvia	New Zealand	0.0000040
Czech Republic	New Caledonia	0.0000040
Czech Republic	Uruguay	0.0000040
Latvia	New Caledonia	0.0000040
Latvia	Uruguay	0.0000040
Switzerland	Japan	0.0000040
Czech Republic	Panama	0.0000040
Latvia	Panama	0.0000040
Czech Republic	South Korea	0.0000040
Latvia	South Korea	0.0000040
India	Switzerland	0.0000040
Norway	Russia	0.0000040
Latvia	Italy	0.0000040
Czech Republic	Taiwan	0.0000040
Latvia	Taiwan	0.0000040
India	Czech Republic	0.0000038
Norway	Iran	0.0000037
China	South Africa	0.0000037
South Africa	Austria	0.0000037
Greece	Hungary	0.0000036
Kenya	Israel	0.0000036
Kenya	France	0.0000036
Denmark	Argentina	0.0000036
South Africa	China	0.0000035
Israel	Czech Republic	0.0000035
Saudi Arabia	Germany	0.0000035
Nigeria	United Kingdom	0.0000035
Luxembourg	Ireland	0.0000035
Saudi Arabia	Switzerland	0.0000034
New Zealand	Hungary	0.0000034
Nepal	Belgium	0.0000034
Malaysia	Australia	0.0000034
Malaysia	New Zealand	0.0000034
Malaysia	Unknown	0.0000034
Nigeria	Germany	0.0000034
Egypt	Netherlands	0.0000032
Germany	Bulgaria	0.0000031
Brazil	Colombia	0.0000031
South Africa	Brazil	0.0000031
Austria	Switzerland	0.0000030
Brazil	Portugal	0.0000030
Bulgaria	Israel	0.0000029
Switzerland	South Africa	0.0000029
Canada	Finland	0.0000029
Czech Republic	Ireland	0.0000029
Brazil	Chile	0.0000029
British Virgin Islands	Unknown	0.0000029
Argentina	Chile	0.0000029
Colombia	Chile	0.0000029
Kenya	Chile	0.0000029
Spain	Chile	0.0000029
Latvia	Ireland	0.0000029
Iceland	United States	0.0000029
Portugal	Denmark	0.0000028
Italy	Canada	0.0000028
Belgium	Belgium	0.0000027
Israel	Belgium	0.0000027
United Kingdom	Curaçao	0.0000027
Luxembourg	Belgium	0.0000027
Canada	Argentina	0.0000027
Poland	Bulgaria	0.0000027
Israel	Peru	0.0000026
Germany	Japan	0.0000026
Uganda	Sweden	0.0000025
South Africa	Poland	0.0000025
Egypt	Denmark	0.0000025
Egypt	Austria	0.0000025
British Virgin Islands	United Kingdom	0.0000024
South Africa	Norway	0.0000024
Luxembourg	Switzerland	0.0000024
South Africa	New Zealand	0.0000024
South Africa	New Caledonia	0.0000024
South Africa	Uruguay	0.0000024
South Africa	Panama	0.0000024
South Africa	South Korea	0.0000024
South Africa	Taiwan	0.0000024
Egypt	China	0.0000023
Nepal	Peru	0.0000023
United Kingdom	Bulgaria	0.0000023
Saudi Arabia	United States	0.0000023
Costa Rica	Belgium	0.0000023
Canada	Russia	0.0000023
Czech Republic	Belgium	0.0000023
Latvia	Belgium	0.0000023
South Africa	Iran	0.0000023
Unknown	Slovakia	0.0000023
Bulgaria	Colombia	0.0000022
Ireland	Australia	0.0000022
Egypt	Canada	0.0000022
Nepal	Netherlands	0.0000021
Austria	Czech Republic	0.0000021
Kenya	South Africa	0.0000021
China	Vietnam	0.0000021
Mexico	Sweden	0.0000021
Egypt	Brazil	0.0000020
Czech Republic	Switzerland	0.0000020
Latvia	Switzerland	0.0000020
India	Brazil	0.0000020
Argentina	Sweden	0.0000020
Kenya	Sweden	0.0000019
South Africa	Sweden	0.0000018
Slovenia	Denmark	0.0000017
Indonesia	Germany	0.0000017
Ireland	United Kingdom	0.0000017
South Africa	Ireland	0.0000017
Indonesia	Switzerland	0.0000017
France	Finland	0.0000017
Denmark	Romania	0.0000017
Nepal	Romania	0.0000017
Nepal	Spain	0.0000017
Ireland	Germany	0.0000017
Egypt	Poland	0.0000016
China	Mauritius	0.0000016
Denmark	Mauritius	0.0000016
Nepal	Mauritius	0.0000016
Nigeria	Netherlands	0.0000016
Egypt	Norway	0.0000016
Egypt	New Zealand	0.0000016
Egypt	New Caledonia	0.0000016
Egypt	Uruguay	0.0000016
Egypt	Panama	0.0000016
Egypt	South Korea	0.0000016
Egypt	Italy	0.0000016
Egypt	Taiwan	0.0000016
Luxembourg	Czech Republic	0.0000016
Costa Rica	Peru	0.0000016
Hungary	Germany	0.0000015
Portugal	China	0.0000015
Greenland	Sweden	0.0000015
Bangladesh	Unknown	0.0000015
Costa Rica	Netherlands	0.0000014
Brazil	South Africa	0.0000014
Portugal	Singapore	0.0000014
Nigeria	Austria	0.0000014
Canada	Singapore	0.0000013
South Korea	South Africa	0.0000013
Latvia	Czech Republic	0.0000013
Denmark	India	0.0000013
Uganda	France	0.0000013
Finland	Colombia	0.0000013
Colombia	Croatia	0.0000013
Saudi Arabia	Unknown	0.0000012
South Africa	Switzerland	0.0000012
Denmark	Bulgaria	0.0000012
Israel	China	0.0000012
Nigeria	China	0.0000012
Netherlands	Singapore	0.0000012
Egypt	Ireland	0.0000012
Taiwan	Taiwan	0.0000012
Indonesia	United States	0.0000011
Israel	Spain	0.0000011
Belgium	Romania	0.0000011
Brazil	Romania	0.0000011
Costa Rica	Romania	0.0000011
Costa Rica	Spain	0.0000011
Israel	Romania	0.0000011
Nigeria	Canada	0.0000011
France	Turkey	0.0000011
New Caledonia	Sweden	0.0000011
Nigeria	Peru	0.0000011
Belgium	Mauritius	0.0000011
Brazil	Mauritius	0.0000011
Costa Rica	Mauritius	0.0000011
Israel	Mauritius	0.0000011
Switzerland	Singapore	0.0000011
Taiwan	Poland	0.0000011
Nigeria	Denmark	0.0000011
Mexico	France	0.0000011
Israel	Brazil	0.0000010
Nigeria	Brazil	0.0000010
Niue	United Kingdom	0.0000010
Switzerland	Croatia	0.0000010
Canada	Hungary	0.0000010
Finland	Portugal	0.0000010
Nepal	United Kingdom	0.0000010
Nepal	France	0.0000010
Chile	Iran	0.0000009
Ireland	Denmark	0.0000009
Chile	Colombia	0.0000009
Canada	Bulgaria	0.0000009
Egypt	Belgium	0.0000009
Slovenia	Poland	0.0000009
Slovenia	Austria	0.0000009
Slovenia	United States	0.0000009
Nigeria	South Africa	0.0000009
Japan	Argentina	0.0000008
Ukraine	Iran	0.0000008
Nigeria	Poland	0.0000008
Ireland	Netherlands	0.0000008
Nigeria	Norway	0.0000008
Egypt	Switzerland	0.0000008
Israel	New Zealand	0.0000008
Nigeria	New Zealand	0.0000008
Belgium	Uruguay	0.0000008
Israel	New Caledonia	0.0000008
Israel	Uruguay	0.0000008
Nigeria	New Caledonia	0.0000008
Nigeria	Uruguay	0.0000008
Belgium	Panama	0.0000008
Israel	Panama	0.0000008
Nigeria	Panama	0.0000008
Israel	South Korea	0.0000008
Nigeria	South Korea	0.0000008
Israel	Italy	0.0000008
Israel	Taiwan	0.0000008
Nigeria	Taiwan	0.0000008
South Africa	Czech Republic	0.0000008
Slovenia	France	0.0000008
Germany	Croatia	0.0000008
Greenland	France	0.0000008
Japan	Vietnam	0.0000007
China	Finland	0.0000007
Germany	Singapore	0.0000007
Australia	Colombia	0.0000007
Austria	Curaçao	0.0000007
New Zealand	Colombia	0.0000007
Costa Rica	United Kingdom	0.0000007
Costa Rica	France	0.0000006
Taiwan	Italy	0.0000006
Indonesia	Unknown	0.0000006
Ireland	Austria	0.0000006
Spain	Bulgaria	0.0000006
Japan	Finland	0.0000006
United States	Luxembourg	0.0000006
Ireland	China	0.0000006
Israel	Ireland	0.0000006
Nigeria	Ireland	0.0000006
Ireland	Canada	0.0000005
Slovenia	Canada	0.0000005
Ireland	Peru	0.0000005
New Zealand	South Africa	0.0000005
Egypt	Czech Republic	0.0000005
Ireland	Brazil	0.0000005
Norway	Colombia	0.0000005
Slovenia	Netherlands	0.0000005
Slovenia	United Kingdom	0.0000005
Colombia	Colombia	0.0000005
Denmark	Russia	0.0000005
Spain	Russia	0.0000005
Nigeria	Belgium	0.0000005
Unknown	Sri Lanka	0.0000005
Ukraine	Colombia	0.0000004
Ireland	Poland	0.0000004
Ireland	Norway	0.0000004
Israel	Switzerland	0.0000004
Nigeria	Switzerland	0.0000004
Ireland	New Zealand	0.0000004
Ireland	New Caledonia	0.0000004
Ireland	Uruguay	0.0000004
Ireland	Panama	0.0000004
Ireland	South Korea	0.0000004
Ireland	Taiwan	0.0000004
Uzbekistan	Sweden	0.0000004
Netherlands	Argentina	0.0000004
Nepal	Canada	0.0000004
Antarctica	Israel	0.0000003
China	Bulgaria	0.0000003
Mexico	South Africa	0.0000003
Spain	India	0.0000003
Germany	India	0.0000003
China	Iran	0.0000003
Denmark	Iran	0.0000003
Nepal	Iran	0.0000003
United Arab Emirates	Italy	0.0000003
United Kingdom	Mexico	0.0000003
Slovenia	Czech Republic	0.0000003
Japan	Bulgaria	0.0000003
Kosovo	Ireland	0.0000003
Ireland	Ireland	0.0000003
Portugal	Poland	0.0000003
Uganda	Colombia	0.0000003
Italy	Ireland	0.0000003
Nigeria	Czech Republic	0.0000003
Bulgaria	Turkey	0.0000003
Japan	India	0.0000003
Nepal	Australia	0.0000003
Switzerland	Bulgaria	0.0000003
China	Japan	0.0000003
Antarctica	Croatia	0.0000003
United Kingdom	Croatia	0.0000003
Nepal	Sweden	0.0000002
Poland	Spain	0.0000002
Denmark	Japan	0.0000002
Denmark	Portugal	0.0000002
Costa Rica	Canada	0.0000002
Ireland	Belgium	0.0000002
Mexico	Colombia	0.0000002
New Zealand	Bulgaria	0.0000002
Peru	United States	0.0000002
Argentina	Colombia	0.0000002
Belgium	Iran	0.0000002
Brazil	Iran	0.0000002
Costa Rica	Iran	0.0000002
Israel	Iran	0.0000002
Kenya	Colombia	0.0000002
Unknown	Mexico	0.0000002
Denmark	Hungary	0.0000002
Spain	Hungary	0.0000002
Ireland	Switzerland	0.0000002
Australia	India	0.0000002
Uzbekistan	France	0.0000002
India	Sweden	0.0000002
Canada	Japan	0.0000002
Canada	Portugal	0.0000002
Brazil	Saudi Arabia	0.0000002
Belgium	South Africa	0.0000002
France	South Africa	0.0000002
Japan	South Africa	0.0000002
Italy	Taiwan	0.0000002
South Korea	Austria	0.0000002
Switzerland	Colombia	0.0000002
Costa Rica	Sweden	0.0000002
Greenland	Colombia	0.0000002
Unknown	Latvia	0.0000002
Japan	Japan	0.0000001
Netherlands	Bulgaria	0.0000001
Iceland	Netherlands	0.0000001
Finland	Bulgaria	0.0000001
Spain	Colombia	0.0000001
Ireland	Czech Republic	0.0000001
Sweden	Bulgaria	0.0000001
United Kingdom	Turkey	0.0000001
Luxembourg	Sweden	0.0000001
Belgium	Bulgaria	0.0000001
United States	Tunisia	0.0000001
Portugal	Italy	0.0000001
Chile	Turkey	0.0000001
United States	Mexico	0.0000001
Switzerland	Mexico	0.0000001
Latvia	Sweden	0.0000001
Germany	Turkey	0.0000001
Taiwan	Bulgaria	0.0000001
Portugal	Hungary	0.0000001
Australia	Turkey	0.0000001
Sweden	Colombia	0.0000001
New Zealand	Turkey	0.0000001
Russia	United States	0.0000001
Slovenia	Germany	0.0000001
Poland	Colombia	0.0000001
Nepal	Denmark	0.0000001
China	Singapore	0.0000001
Netherlands	Hungary	0.0000001
United Kingdom	Finland	0.0000001
Bulgaria	India	0.0000001
Switzerland	Hungary	0.0000001
Luxembourg	France	0.0000001
United States	Slovakia	0.0000001
Japan	Portugal	0.0000001
Norway	Turkey	0.0000001
Colombia	Turkey	0.0000001
Australia	Vietnam	0.0000001
Finland	Vietnam	0.0000001
United Kingdom	Vietnam	0.0000001
Latvia	France	0.0000001
Portugal	Luxembourg	0.0000001
Ukraine	Turkey	0.0000001
Portugal	India	0.0000000
Costa Rica	Denmark	0.0000000
South Korea	United States	0.0000000
Netherlands	Luxembourg	0.0000000
Germany	Hungary	0.0000000
Uzbekistan	Colombia	0.0000000
Hungary	France	0.0000000
Egypt	Sweden	0.0000000
Netherlands	India	0.0000000
Colombia	India	0.0000000
Switzerland	Luxembourg	0.0000000
Switzerland	India	0.0000000
China	Israel	0.0000000
Hungary	Italy	0.0000000
Italy	Austria	0.0000000
Hungary	United Kingdom	0.0000000
Finland	Turkey	0.0000000
Uganda	Turkey	0.0000000
Germany	Luxembourg	0.0000000
Mexico	Turkey	0.0000000
Argentina	Turkey	0.0000000
Netherlands	Portugal	0.0000000
Kenya	Turkey	0.0000000
Japan	Singapore	0.0000000
Australia	Finland	0.0000000
Bulgaria	Argentina	0.0000000
Egypt	France	0.0000000
Nigeria	Sweden	0.0000000
Switzerland	Turkey	0.0000000
Greenland	Turkey	0.0000000
Greece	United Kingdom	0.0000000
Iceland	United Kingdom	0.0000000
Singapore	United Kingdom	0.0000000
Spain	Turkey	0.0000000
Luxembourg	Colombia	0.0000000
Brazil	Mexico	0.0000000
United States	Sri Lanka	0.0000000
Czech Republic	Colombia	0.0000000
Latvia	Colombia	0.0000000
Nigeria	France	0.0000000
Ireland	Sweden	0.0000000
Portugal	Tunisia	0.0000000
Sweden	Turkey	0.0000000
Chile	Argentina	0.0000000
Bulgaria	Finland	0.0000000
Poland	Turkey	0.0000000
Netherlands	Tunisia	0.0000000
Switzerland	Tunisia	0.0000000
Germany	Argentina	0.0000000
France	India	0.0000000
United Kingdom	Japan	0.0000000
Nepal	Poland	0.0000000
Australia	Japan	0.0000000
South Africa	Colombia	0.0000000
Australia	Argentina	0.0000000
Finland	Japan	0.0000000
Brazil	Turkey	0.0000000
Austria	Finland	0.0000000
New Zealand	Argentina	0.0000000
Hungary	Switzerland	0.0000000
France	Japan	0.0000000
France	Portugal	0.0000000
Hong Kong	United Kingdom	0.0000000
Canada	India	0.0000000
Germany	Tunisia	0.0000000
Portugal	Czech Republic	0.0000000
Portugal	Slovakia	0.0000000
Uzbekistan	Turkey	0.0000000
United Arab Emirates	United Kingdom	0.0000000
Costa Rica	Poland	0.0000000
Norway	Argentina	0.0000000
Egypt	Colombia	0.0000000
Colombia	Argentina	0.0000000
Peru	Netherlands	0.0000000
Netherlands	Slovakia	0.0000000
China	Hungary	0.0000000
Ukraine	Argentina	0.0000000
United States	Latvia	0.0000000
Switzerland	Slovakia	0.0000000
Chile	Finland	0.0000000
Hong Kong	Canada	0.0000000
India	Bulgaria	0.0000000
Greece	Austria	0.0000000
Chile	Bulgaria	0.0000000
Nepal	Italy	0.0000000
China	India	0.0000000
Finland	Argentina	0.0000000
China	Luxembourg	0.0000000
Uganda	Argentina	0.0000000
Germany	Slovakia	0.0000000
New Zealand	Finland	0.0000000
Hungary	Poland	0.0000000
United Arab Emirates	Netherlands	0.0000000
Belgium	Colombia	0.0000000
Israel	Colombia	0.0000000
Nigeria	Colombia	0.0000000
Mexico	Argentina	0.0000000
China	Turkey	0.0000000
Argentina	Argentina	0.0000000
Kenya	Argentina	0.0000000
Austria	Turkey	0.0000000
Costa Rica	Italy	0.0000000
Norway	Finland	0.0000000
Israel	Bulgaria	0.0000000
Colombia	Finland	0.0000000
Norway	Bulgaria	0.0000000
Ukraine	Finland	0.0000000
Switzerland	Argentina	0.0000000
Luxembourg	Turkey	0.0000000
Niue	United States	0.0000000
Greenland	Argentina	0.0000000
Argentina	Bulgaria	0.0000000
Japan	Hungary	0.0000000
Bulgaria	Japan	0.0000000
Bulgaria	Portugal	0.0000000
Ukraine	Bulgaria	0.0000000
Spain	Argentina	0.0000000
Czech Republic	Turkey	0.0000000
Latvia	Turkey	0.0000000
Ireland	Colombia	0.0000000
British Virgin Islands	Netherlands	0.0000000
Uganda	Finland	0.0000000
United Kingdom	India	0.0000000
Portugal	Sri Lanka	0.0000000
Antarctica	India	0.0000000
Hong Kong	Austria	0.0000000
Japan	Luxembourg	0.0000000
Uganda	Bulgaria	0.0000000
Mexico	Finland	0.0000000
Netherlands	Sri Lanka	0.0000000
Argentina	Finland	0.0000000
Costa Rica	Finland	0.0000000
Kenya	Finland	0.0000000
South Africa	Turkey	0.0000000
Switzerland	Sri Lanka	0.0000000
Sweden	Argentina	0.0000000
Mexico	Bulgaria	0.0000000
Uganda	Spain	0.0000000
United Kingdom	Portugal	0.0000000
Poland	Argentina	0.0000000
Kenya	Bulgaria	0.0000000
Slovenia	Sweden	0.0000000
Argentina	Spain	0.0000000
Greenland	Finland	0.0000000
Chile	Japan	0.0000000
Chile	Lithuania	0.0000000
Chile	Portugal	0.0000000
Mexico	Spain	0.0000000
Spain	Finland	0.0000000
Kenya	Spain	0.0000000
Brazil	Argentina	0.0000000
Egypt	Turkey	0.0000000
Germany	Sri Lanka	0.0000000
Germany	Portugal	0.0000000
Greenland	Bulgaria	0.0000000
China	Tunisia	0.0000000
Australia	Lithuania	0.0000000
Australia	Portugal	0.0000000
New Zealand	Japan	0.0000000
New Zealand	Portugal	0.0000000
Portugal	Russia	0.0000000
Israel	Finland	0.0000000
Greenland	Spain	0.0000000
Uzbekistan	Argentina	0.0000000
Netherlands	Russia	0.0000000
Portugal	Latvia	0.0000000
Switzerland	Russia	0.0000000
Norway	Japan	0.0000000
Norway	Lithuania	0.0000000
Norway	Portugal	0.0000000
Sweden	Finland	0.0000000
Colombia	Japan	0.0000000
Colombia	Portugal	0.0000000
Netherlands	Latvia	0.0000000
Ukraine	Japan	0.0000000
Ukraine	Lithuania	0.0000000
Ukraine	Portugal	0.0000000
Hungary	Netherlands	0.0000000
Poland	Finland	0.0000000
Switzerland	Latvia	0.0000000
Belgium	Turkey	0.0000000
Israel	Turkey	0.0000000
Nigeria	Turkey	0.0000000
China	Slovakia	0.0000000
Portugal	Ireland	0.0000000
Brazil	Finland	0.0000000
Austria	Bulgaria	0.0000000
Germany	Latvia	0.0000000
Brazil	Bulgaria	0.0000000
Finland	Lithuania	0.0000000
Japan	Tunisia	0.0000000
Uganda	Japan	0.0000000
Uganda	Lithuania	0.0000000
Uganda	Portugal	0.0000000
Slovenia	Bulgaria	0.0000000
Uzbekistan	Finland	0.0000000
Mexico	Japan	0.0000000
Mexico	Lithuania	0.0000000
Mexico	Portugal	0.0000000
Argentina	Japan	0.0000000
Argentina	Lithuania	0.0000000
Argentina	Portugal	0.0000000
Kenya	Japan	0.0000000
Kenya	Lithuania	0.0000000
Kenya	Portugal	0.0000000
Uzbekistan	Bulgaria	0.0000000
Ireland	Turkey	0.0000000
Luxembourg	Argentina	0.0000000
Switzerland	Lithuania	0.0000000
Switzerland	Portugal	0.0000000
Uzbekistan	Spain	0.0000000
Czech Republic	Argentina	0.0000000
Latvia	Argentina	0.0000000
Australia	Hungary	0.0000000
Finland	Hungary	0.0000000
United Kingdom	Hungary	0.0000000
Greenland	Japan	0.0000000
Greenland	Lithuania	0.0000000
Greenland	Portugal	0.0000000
Spain	Japan	0.0000000
Spain	Portugal	0.0000000
Japan	Slovakia	0.0000000
Portugal	Mexico	0.0000000
Netherlands	Mexico	0.0000000
Australia	Luxembourg	0.0000000
Finland	Luxembourg	0.0000000
United Kingdom	Luxembourg	0.0000000
South Africa	Argentina	0.0000000
Finland	India	0.0000000
Hong Kong	Switzerland	0.0000000
Luxembourg	Finland	0.0000000
Sweden	Japan	0.0000000
Sweden	Lithuania	0.0000000
Sweden	Portugal	0.0000000
China	Sri Lanka	0.0000000
Poland	Japan	0.0000000
Poland	Lithuania	0.0000000
Poland	Portugal	0.0000000
Luxembourg	Bulgaria	0.0000000
Czech Republic	Finland	0.0000000
Egypt	Argentina	0.0000000
Latvia	Finland	0.0000000
Brazil	Japan	0.0000000
Brazil	Lithuania	0.0000000
Czech Republic	Bulgaria	0.0000000
Latvia	Bulgaria	0.0000000
Luxembourg	Spain	0.0000000
Czech Republic	Spain	0.0000000
Latvia	Spain	0.0000000
Uzbekistan	Japan	0.0000000
Uzbekistan	Lithuania	0.0000000
Uzbekistan	Portugal	0.0000000
Portugal	Uruguay	0.0000000
South Africa	Finland	0.0000000
China	Russia	0.0000000
South Africa	Bulgaria	0.0000000
Belgium	Argentina	0.0000000
Israel	Argentina	0.0000000
Nigeria	Argentina	0.0000000
Japan	Sri Lanka	0.0000000
China	Latvia	0.0000000
Egypt	Finland	0.0000000
Slovenia	Spain	0.0000000
Egypt	Bulgaria	0.0000000
Australia	Tunisia	0.0000000
Finland	Tunisia	0.0000000
United Kingdom	Tunisia	0.0000000
China	Portugal	0.0000000
Egypt	Spain	0.0000000
Ireland	Argentina	0.0000000
Austria	Japan	0.0000000
Austria	Lithuania	0.0000000
Austria	Portugal	0.0000000
Luxembourg	Japan	0.0000000
Luxembourg	Lithuania	0.0000000
Luxembourg	Portugal	0.0000000
Belgium	Finland	0.0000000
Nigeria	Finland	0.0000000
Japan	Russia	0.0000000
Czech Republic	Japan	0.0000000
Czech Republic	Lithuania	0.0000000
Czech Republic	Portugal	0.0000000
Latvia	Japan	0.0000000
Latvia	Lithuania	0.0000000
Latvia	Portugal	0.0000000
Nigeria	Bulgaria	0.0000000
Australia	Slovakia	0.0000000
Finland	Slovakia	0.0000000
United Kingdom	Slovakia	0.0000000
Japan	Latvia	0.0000000
Nigeria	Spain	0.0000000
South Africa	Japan	0.0000000
South Africa	Lithuania	0.0000000
South Africa	Portugal	0.0000000
Ireland	Finland	0.0000000
China	Mexico	0.0000000
Ireland	Bulgaria	0.0000000
Egypt	Japan	0.0000000
Egypt	Lithuania	0.0000000
Egypt	Portugal	0.0000000
Belgium	Japan	0.0000000
Belgium	Lithuania	0.0000000
Belgium	Portugal	0.0000000
Israel	Japan	0.0000000
Israel	Lithuania	0.0000000
Israel	Portugal	0.0000000
Nigeria	Japan	0.0000000
Nigeria	Lithuania	0.0000000
Nigeria	Portugal	0.0000000
Japan	Mexico	0.0000000
Australia	Sri Lanka	0.0000000
Finland	Sri Lanka	0.0000000
United Kingdom	Sri Lanka	0.0000000
Ireland	Japan	0.0000000
Ireland	Lithuania	0.0000000
Ireland	Portugal	0.0000000
Finland	Russia	0.0000000
Australia	Latvia	0.0000000
Finland	Latvia	0.0000000
United Kingdom	Latvia	0.0000000
Australia	Mexico	0.0000000
Finland	Mexico	0.0000000
France	Nigeria	0.0000000

dependency_summary_noUnknown <- dependency_summary %>%
                                    filter(Cited_Country != "Unknown" & Citing_Country != "Unknown")%>%
                                    arrange(desc(Total_Dependency_Fraction))

dependency_summary <- dependency_summary %>%
                                    arrange(desc(Total_Dependency_Fraction))

3.2.2 Sectors

### select dependency information for slugs and packages
cran_github_rdi <- cran_github %>%
                      select(Package, slug, Depends)

### rename columns
colnames(cran_github_rdi) <- c("Citing_Package", "slug", "Dependencies")


### Package citation column will be the unlisted dependencies column
cran_github_rdi$Package_Citation <- cran_github_rdi$Dependencies


### join commits information for the citing packages
cran_github_RDI <- cran_github_rdi %>%
                      inner_join(user_commits_total, by = "slug")%>%
                        select(Citing_Package, slug, Dependencies, login,
                              sector, total_additions, total_code_for_slug,
                              contribution_fraction_loc, Package_Citation) %>%
                       # Remove rows with NA in Depends
                        filter(!is.na(Package_Citation))

### rename columns on the basis of the citing package
colnames(cran_github_RDI) <- c("Citing_Package", "Citing_Slug", "Dependencies", "Citing_Login",  "Citing_Sector",
                                "Citing_Additions", "Citing_Total_Slug_Additions", "Citing_Package_Fraction" , "Package_Citation")


### unlist the dependencies for joining
cran_github_RDI_network <-  cran_github_RDI %>%
  separate_rows(Package_Citation, sep = ",\\s*") %>%
  filter(Package_Citation != "")


#### prepare commits information for cited packages
user_commits_rdi <- user_commits_total %>%
  mutate(Package_Citation = str_split(slug, "/", simplify = TRUE)[, 2])%>%
  select(login, sector, total_additions, total_code_for_slug, contribution_fraction_loc, Package_Citation)
  
  colnames(user_commits_rdi) <- c( "Cited_Login", "Cited_Sector", 
                                   "Cited_Additions", "Cited_Total_Slug_Additions", "Cited_Package_Fraction", "Package_Citation")
  
  ### join cited package commit information to citing package dataframe
  cran_github_rdi_full <- cran_github_RDI_network %>%
                                        inner_join(user_commits_rdi, by = "Package_Citation")

  ### create dependency_fraction = citing package fraction multiplied by cited package fraction 
  cran_github_rdi_grouped <- cran_github_rdi_full %>%
  mutate(Dependency_Fraction = Citing_Package_Fraction * Cited_Package_Fraction)

# Group by Cited Country and Citing Country, and sum Dependency_Fraction

### the number of citations made from one country to another is simply the sum of the fractioned scores associated with each pair, with the sum across all possible pairs adding up to the total number of citations made at the world level.

dependency_summary <- cran_github_rdi_grouped %>%
  group_by(Cited_Sector, Citing_Sector) %>%
  summarize(Total_Dependency_Fraction = sum(Dependency_Fraction, na.rm = TRUE))

sum(dependency_summary$Total_Dependency_Fraction)

[1] 589

# Group by Cited Country and sum Total_Dependency_Fraction - total number of citations attributed to each country
citations_by_sector <- dependency_summary %>%
  group_by(Cited_Sector) %>%
  summarize(Fraction_of_Citations = round(sum(Total_Dependency_Fraction, na.rm = TRUE), 4))


sum(citations_by_sector$Fraction_of_Citations)

[1] 589

citations_by_sector$Denominator_RDI <- round(citations_by_sector$Fraction_of_Citations / sum(citations_by_sector$Fraction_of_Citations),4)

# Group by citing country and sum Total_Dependency_Fraction - total number of citations made by each country
citings_by_sector <- dependency_summary %>%
  group_by(Citing_Sector) %>%
  summarize(Fraction_of_Citings = round(sum(Total_Dependency_Fraction, na.rm = TRUE), 4))


sum(citings_by_sector$Fraction_of_Citings)

[1] 588.9999

# join citings by country with dependency_summary

citings_dependency_summary <- citings_by_sector %>%
                                full_join(dependency_summary, by = "Citing_Sector")

citings_dependency_summary$Numerator_RDI <- round(citings_dependency_summary$Total_Dependency_Fraction / citings_dependency_summary$Fraction_of_Citings,4)

## join denominator_RDI

citations_citings_dependency_summary <- citations_by_sector %>%
                                full_join(citings_dependency_summary, by = "Cited_Sector") %>%
                                select(Citing_Sector, Cited_Sector, Numerator_RDI, Denominator_RDI)

citations_citings_dependency_summary$RDI <- round(citations_citings_dependency_summary$Numerator_RDI / citations_citings_dependency_summary$Denominator_RDI,4)

3.2.3 Double-sided weight graph

# Calculate the total of Fraction_of_Citations, including "Unknown"
total_citations_incl_unknown <- sum(citations_by_country$Fraction_of_Citations)

# Create and round the percentage column to the nearest hundredth, including "Unknown" in the percentage calculation
citations_by_country$Percentage_of_Citations <- round(
  (citations_by_country$Fraction_of_Citations / total_citations_incl_unknown) * 100, 2
)

# Arrange by descending order of the new percentage column
 citations_by_country %>%
  arrange(desc(Percentage_of_Citations))

# A tibble: 70 × 4
   Cited_Country Fraction_of_Citations Denominator_RDI Percentage_of_Citations
   <chr>                         <dbl>           <dbl>                   <dbl>
 1 Unknown                      200.            0.340                    34.0 
 2 United States                172.            0.292                    29.2 
 3 Germany                       41.3           0.0702                    7.02
 4 France                        30.6           0.0519                    5.19
 5 Denmark                       23.1           0.0393                    3.93
 6 Canada                        18.6           0.0316                    3.16
 7 Norway                        14.9           0.0252                    2.52
 8 Netherlands                   12.4           0.0211                    2.11
 9 Bulgaria                      11.2           0.019                     1.9 
10 Australia                      9.99          0.017                     1.7 
# ℹ 60 more rows

# Calculate the total of Fraction_of_Citations, excluding "Unknown"
total_citations <- sum(citations_by_country$Fraction_of_Citations[citations_by_country$Cited_Country != "Unknown"])

# Create and round the percentage column to the nearest hundredth, excluding "Unknown" in the percentage calculation
citations_by_country$Percentage_of_Citations <- ifelse(
  citations_by_country$Cited_Country == "Unknown", 
  NA, 
  round((citations_by_country$Fraction_of_Citations / total_citations) * 100, 2)
)



citations_by_country %>%
  arrange(desc(Percentage_of_Citations))

# A tibble: 70 × 4
   Cited_Country  Fraction_of_Citations Denominator_RDI Percentage_of_Citations
   <chr>                          <dbl>           <dbl>                   <dbl>
 1 United States                 172.            0.292                    44.2 
 2 Germany                        41.3           0.0702                   10.6 
 3 France                         30.6           0.0519                    7.87
 4 Denmark                        23.1           0.0393                    5.95
 5 Canada                         18.6           0.0316                    4.79
 6 Norway                         14.9           0.0252                    3.83
 7 Netherlands                    12.4           0.0211                    3.19
 8 Bulgaria                       11.2           0.019                     2.88
 9 Australia                       9.99          0.017                     2.57
10 United Kingdom                  9.65          0.0164                    2.48
# ℹ 60 more rows

The following graph shows a countries’ lines of code credit compared to the percentage of citations they have from other countries (or reverse dependencies in package labguage)

data <- data.frame(
  Country = c("United States", "Germany", "United Kingdom", "France", "Canada", 
              "Australia", "Netherlands", "Switzerland", "Spain", "China",
              "United States", "Germany", "United Kingdom", "France", "Canada", 
              "Australia", "Netherlands", "Switzerland", "Spain", "China"),
  Measure = c("Package %", "Package %", "Package %", "Package %", "Package %", 
              "Package %", "Package %", "Package %", "Package %", "Package %",
              "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %", 
              "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %", "Reverse Dependency %"),
  Value = c( -30.9, -10.6, -7.6, -5.9, -5.3,  -4.7, -3.6, -2.7, -2.6, -2.2, 
             44.2, 10.6, 2.48, 7.9, 4.8, 2.6, 3.2, 1, 1.4, .3) # Negative for Dependency %, positive for Code%
)

# Filter data to include only Code % values for ordering
code_values <- data %>% 
  filter(Measure == "Package %") %>% 
  arrange(desc(Value))

# Reorder Country factor based on Code % values
data$Country <- factor(data$Country, levels = code_values$Country)

# Create the plot with value labels and ordered countries
# Create the plot with value labels and ordered countries with increased text size
plot <- ggplot(data, aes(x = Country, y = Value, fill = Measure)) +
  geom_bar(stat = "identity", position = "identity") +
  coord_flip() +
  scale_y_continuous(labels = abs, breaks = seq(-50, 50, by = 10), limits = c(-50, 55)) +
  labs(y = "Percentage", x = "", title = "R") +
  theme_minimal() +
  scale_fill_manual(values = c("Package %" = "darkblue", "Reverse Dependency %" = "lightblue")) + # Add your own colors
  theme(
    text = element_text(size = 14), # Changes global text size
    axis.title = element_text(size = 16), # Changes axis title text size
    axis.text = element_text(size = 12), # Changes axis text size
    plot.title = element_text(size = 12, face = "bold", hjust = .5) # Changes plot title text size and makes it bold
  )+
  geom_text(data = subset(data, Value > 0), aes(label = sprintf("%0.1f%%", Value)), 
            position = position_nudge(y = 0.5), hjust = 0, size = 3.5) +
  geom_text(data = subset(data, Value < 0), aes(label = sprintf("%0.1f%%", abs(Value))), 
            position = position_nudge(y = -0.5), hjust = 1, size = 3.5)+
  theme(legend.position = "bottom")

# Display the plot
print(plot)

3.3 Measuring Impact: OSS Developers and Projects

• Who are the key players (projects, developers, institutions, sectors, and countries) on the networks and how has this changed over time?

• How do the positions of OSS actors impact OSS contributions?

3.3.1 Distributions of impact measures by top actors

3.3.1.1 What is the distribution of impact measures among sectors?

We take a look at the distributions of some of the impact measures by the sectors to see if certain sectors have packages of more impact.

3.3.1.1.1 All-Time Downloads

It looks like the business sector has packages with the highest all-time downloads on average. This is looking at the log of the downloads for visual purposes.

## Show distribution of downloads by Institution
 ggplot(cran_repos, aes(x = Sector, y = log(Downloads_All_Time), fill = Sector))+
  geom_boxplot()+
  ggtitle("All-Time Downloads Distribution by Sector (GitHub R Packages)")+
  ylab("Log of All-Time Downloads")+
   theme_gdocs()+
    theme(plot.title = element_text(size = 13))+
   coord_flip()+
  xlab("")+
   scale_fill_westat(option = "BLUES", drop = FALSE)

3.3.1.1.2 Normalized Downloads

The same is true of normalized downloads as well… These are probably packages from Rstudio.

## Show distribution of downloads by Institution
 ggplot(cran_repos, aes(x = Sector, y = log(Downloads_Normalized), fill = Sector))+
  geom_boxplot()+
  ggtitle("Normalized Downloads Distribution by Sector (GitHub R Packages)")+
  ylab("Log of Normalized Downloads")+
   theme_gdocs()+
    theme(plot.title = element_text(size = 13))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)+
   xlab("")+
   labs(caption = "*Data points represent individual packages")

3.3.1.1.3 Reverse Dependencies

For reverse dependencies, most sectors are at zero on average aside from government and business, which are about 1 on average. Again, this looks at the log of reverse dependencies, so this really means about 10 reverse dependencies on average. There are a lot of observations for Unknown sector that are at the higher end.

## Show distribution of downloads by Institution
 ggplot(cran_repos, aes(x = Sector, y = log(Reverse_Depends_Count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Reverse Dependencies Distribution by Sector (GitHub R Packages)")+
  ylab("Log of Reverse Dependencies")+
   theme_gdocs()+
    theme(plot.title = element_text(size = 13))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)+
   xlab("")

3.3.1.1.4 Stars

Business has the highest log of stars on average, followed by government. We know from the EDA that stars and downloads have a moderate correlation so this makes sense.

## Show distribution of downloads by Institution
 ggplot(cran_repos, aes(x = Sector, y = log(stargazer_count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Star Count Distribution by Sector (GitHub R Packages)")+
  ylab("Log of Star Count")+
   theme_gdocs()+
    theme(plot.title = element_text(size = 13))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)+
   xlab("")+
   labs(caption = "*Data points represent individual packages")

3.3.1.1.5 Forks

Business has the highest log of forks on average as well, followed by government again. We know from the EDA that stars and forks have a very high correlation so this makes sense too.

## Show distribution of downloads by Institution
 ggplot(cran_repos, aes(x = Sector, y = log(fork_count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Fork Count Distribution by Sector (GitHub R Packages)")+
  ylab("Log of Fork Count")+
   theme_gdocs()+
    theme(plot.title = element_text(size = 13))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)+
   xlab("")+
   labs(caption = "*Data points represent individual packages")

3.3.1.2 What is the distribution of impact measures among institutions/organizations?

Of the top institutions/organizations we found on GitHub, we look at the distribution of downloads for these packages.

### Filter for names of the top 5 institutions 
Top_Institution_repos <- cran_repos %>%
                  filter(Institution %in% top10_Institutions_GitHub$Institution)%>%
                  filter(!is.na(Downloads_All_Time))


Top_Institution_repos <- cran_repos %>%
                  filter(Institution %in% top10_Institutions_GitHub$Institution)%>%
                  filter(!is.na(Downloads_All_Time))

3.3.1.2.1 All-Time Downloads

RStudio has the highest log of all-time downloads on average of the top 10 institutions on GitHub.

 ggplot(Top_Institution_repos, aes(x = Institution, y = log(Downloads_All_Time), fill = Sector))+
  geom_boxplot()+
  ggtitle("All-Time Downloads Distribution by Institution - Top 10")+
  ylab("Log of All-Time Downloads")+
   ylim(0,20)+
   theme_gdocs()+
    theme(plot.title = element_text(size = 15))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)

3.3.1.2.2 Normalized Downloads

The distributions are very similar for normalized downloads as well.

 ggplot(Top_Institution_repos, aes(x = Institution, y = log(Downloads_Normalized), fill = Sector))+
  geom_boxplot()+
  ggtitle("Normalized Downloads Distribution by Institution - Top 10")+
  ylab("Log of Normalized Downloads")+
   ylim(0,20)+
   theme_gdocs()+
    theme(plot.title = element_text(size = 15))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)

3.3.1.2.3 Reverse Dependencies

For reverse dependencies, the log averages are essentially all zero. There are a few that are above this mark, with RStudio having the most observations at the higher end.

 ggplot(Top_Institution_repos, aes(x = Institution, y = log(Reverse_Depends_Count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Reverse Dependencies Distribution by Institution - Top 10")+
  ylab("Log of Reverse Dependencies")+
   ylim(0,20)+
   theme_gdocs()+
    theme(plot.title = element_text(size = 15))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)

3.3.1.2.4 Stars

Star count is led by Rstudio as well…UCLA appears to be the next highest on average.

ggplot(Top_Institution_repos, aes(x = Institution, y = log(stargazer_count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Star Count Distribution by Institution - Top 10")+
  ylab("Log of Star Count")+
   ylim(0,20)+
   theme_gdocs()+
    theme(plot.title = element_text(size = 15))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)

3.3.1.2.5 Forks

The same that is true of stars is mostly true for forks as well

ggplot(Top_Institution_repos, aes(x = Institution, y = log(fork_count), fill = Sector))+
  geom_boxplot()+
  ggtitle("Fork Count Distribution by Institution - Top 10")+
  ylab("Log of Fork Count")+
   ylim(0,20)+
   theme_gdocs()+
    theme(plot.title = element_text(size = 15))+
   coord_flip()+
   scale_fill_westat(option = "BLUES", drop = FALSE)

The following code is some analysis for a working paper… results are not commented on at the moment, but we want to see if larger teams have more impact on average.

3.4 How does team size relate to impact?

test removing extreme team size outliers, add reverse dependencies, normalized and non-normalized

Literature on team size and citations - do we see same thing?

## Create team size
user_commits_total <- user_commits_total %>%
  group_by(slug) %>%
  mutate(team_size = n()) %>%
  ungroup()

## normalize stars and forks based on year_created 

user_commits_total <- user_commits_total %>%
  mutate(year_created = as.numeric(year_created))

user_commits_total <- user_commits_total %>%
  mutate(
    normalization_factor = ifelse(is.na(year_created), NA, 2023 - year_created + 1),
    stargazer_count_normalized = ifelse(is.na(stargazer_count) | is.na(normalization_factor), stargazer_count, stargazer_count / normalization_factor),
    fork_count_normalized = ifelse(is.na(fork_count) | is.na(normalization_factor), fork_count, fork_count / normalization_factor),
    reverse_dep_normalized = ifelse(is.na(Reverse_Depends_Count) | is.na(normalization_factor), Reverse_Depends_Count, Reverse_Depends_Count / normalization_factor)
  ) 

3.4.1 Bin teamsize

3.4.1.1 With outliers

user_commits_distinct <- user_commits_total %>%
  distinct(slug, .keep_all = TRUE)


quantile(user_commits_distinct$team_size, probs = seq(0, 1, .1))

  0%  10%  20%  30%  40%  50%  60%  70%  80%  90% 100% 
   1    1    1    1    2    2    3    3    5    8  880 

# Define the bins based on the updated percentiles

# Define the bins based on the updated percentiles
bins <- c(1, 2, 3, 5, 8, 880)

# Create labels for the bins
bin_labels <- c("[1]", "[2]", "[3-4]", "[5-7]", "[8-880]")

# Create a new column with binned team sizes and custom labels
user_commits_distinct <- user_commits_distinct %>%
  mutate(team_size_bin = cut(team_size, breaks = bins, labels = bin_labels, include.lowest = TRUE, right = FALSE))

## convert 
table(user_commits_distinct$team_size_bin)

    [1]     [2]   [3-4]   [5-7] [8-880] 
   2441    1804    1588     791     783 

mean(user_commits_distinct$team_size)

[1] 4.619009

3.4.1.2 No outliers (IQR)

# Calculate Q1, Q3, and IQR
Q1 <- quantile(user_commits_distinct$team_size, 0.25)
Q3 <- quantile(user_commits_distinct$team_size, 0.75)
IQR <- Q3 - Q1

# Define lower and upper bounds
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR

# Filter out outliers
user_commits_no_outliers1 <- user_commits_distinct %>%
  filter(team_size >= lower_bound & team_size <= upper_bound)

quantile(user_commits_no_outliers1$team_size, probs = seq(0, 1, .1))

  0%  10%  20%  30%  40%  50%  60%  70%  80%  90% 100% 
   1    1    1    1    2    2    2    3    4    5    8 

# Define the bins based on the updated percentiles
bins_no_outliers1 <- c(1, 2, 3, 5, 8)

# Create labels for the bins
bin_labels_no_outliers1 <- c("[1]", "[2]", "[3-4]", "[5-8]")


# Create a new column with binned team sizes and custom labels
user_commits_no_outliers1 <- user_commits_no_outliers1 %>%
  mutate(team_size_bin = cut(team_size, breaks = bins_no_outliers1, labels = bin_labels_no_outliers1, include.lowest = TRUE, right = FALSE))

# View the table of binned team sizes
table(user_commits_no_outliers1$team_size_bin)

  [1]   [2] [3-4] [5-8] 
 2441  1804  1588   909 

3.4.1.3 No outliers (z-score)

# Calculate the mean and standard deviation of team_size
mean_team_size <- mean(user_commits_distinct$team_size)
sd_team_size <- sd(user_commits_distinct$team_size)

# Calculate Z-scores
user_commits_distinct <- user_commits_distinct %>%
  mutate(z_score = (team_size - mean_team_size) / sd_team_size)

# Define a threshold for Z-scores (commonly 3 or 2.5)
z_threshold <- 3

# Filter out outliers based on Z-score
user_commits_no_outliers_z <- user_commits_distinct %>%
  filter(abs(z_score) <= z_threshold)

quantile(user_commits_no_outliers_z$team_size, probs = seq(0, 1, .1))

  0%  10%  20%  30%  40%  50%  60%  70%  80%  90% 100% 
 1.0  1.0  1.0  1.0  2.0  2.0  3.0  3.0  5.0  7.1 58.0 

# Define the bins based on the updated percentiles
bins_no_outliers_z <- c(1, 2, 3, 5, 8, 58)

# Create labels for the bins
bin_labels_no_outliers_z <- c("[1]", "[2]", "[3-4]", "[5-6]", "[7-58]")

# Create a new column with binned team sizes and custom labels
user_commits_no_outliers_z <- user_commits_no_outliers_z %>%
  mutate(team_size_bin = cut(team_size, breaks = bins_no_outliers_z , labels = bin_labels_no_outliers_z, include.lowest = TRUE, right = FALSE))

# View the table of binned team sizes
table(user_commits_no_outliers_z$team_size_bin)

   [1]    [2]  [3-4]  [5-6] [7-58] 
  2441   1804   1588    791    736 

3.4.2 boxplot viz (With Outliers)

3.4.2.1 Normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(stargazer_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count Normalized", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(fork_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(Downloads_Normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(reverse_dep_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep Normalized", fill = "Team Size") +
  custom_theme

3.4.2.2 non-normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(stargazer_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(fork_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(Downloads_All_Time), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_distinct, aes(x = team_size_bin, y = log(Reverse_Depends_Count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep", fill = "Team Size") +
  custom_theme

3.4.3 boxplot viz (no outliers IQR)

3.4.3.1 Normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(stargazer_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count Normalized", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(fork_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(Downloads_Normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(reverse_dep_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep Normalized", fill = "Team Size") +
  custom_theme

3.4.3.2 non-normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(stargazer_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(fork_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(Downloads_All_Time), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers1, aes(x = team_size_bin, y = log(Reverse_Depends_Count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep", fill = "Team Size") +
  custom_theme

3.4.4 boxplot viz (no outliers z-score)

3.4.4.1 Normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(stargazer_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count Normalized", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(fork_count_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(Downloads_Normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads Normalized", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(reverse_dep_normalized), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep Normalized", fill = "Team Size") +
  custom_theme

3.4.4.2 non-normalized

# Define a custom theme
custom_theme <- theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
    axis.text.y = element_text(size = 12),
    axis.title = element_text(size = 14, face = "bold"),
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 10)
  )

# Plot stargazer_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(stargazer_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_westat(option = "BLUES", drop = FALSE) +
  labs(x = "Team Size Bin", y = "Stargazer Count", fill = "Team Size") +
  custom_theme

# Plot fork_count_normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(fork_count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set3") +
  labs(x = "Team Size Bin", y = "Fork Count", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(Downloads_All_Time), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Downloads", fill = "Team Size") +
  custom_theme

# Plot Downloads_Normalized vs. team_size_bin
ggplot(user_commits_no_outliers_z, aes(x = team_size_bin, y = log(Reverse_Depends_Count), fill = team_size_bin)) +
  geom_boxplot(outlier.color = "red", outlier.shape = 21, outlier.size = 3) +
  scale_fill_brewer(palette = "Set5") +
  labs(x = "Team Size Bin", y = "Rev Dep", fill = "Team Size") +
  custom_theme

3.4.5 correlation viz (with outliers)

3.4.5.1 normalized

# Calculate correlation between team_size and the normalized counts
correlation_matrix <- user_commits_distinct %>%
  select(team_size, stargazer_count_normalized, fork_count_normalized, Downloads_Normalized, reverse_dep_normalized) %>%
  cor(use = "complete.obs")

# View the correlation matrix
print(correlation_matrix)

                           team_size stargazer_count_normalized
team_size                  1.0000000                 0.75543882
stargazer_count_normalized 0.7554388                 1.00000000
fork_count_normalized      0.7782930                 0.92576329
Downloads_Normalized       0.2759388                 0.10871598
reverse_dep_normalized     0.1787497                 0.07614058
                           fork_count_normalized Downloads_Normalized
team_size                             0.77829300            0.2759388
stargazer_count_normalized            0.92576329            0.1087160
fork_count_normalized                 1.00000000            0.1200080
Downloads_Normalized                  0.12000801            1.0000000
reverse_dep_normalized                0.09906453            0.2785460
                           reverse_dep_normalized
team_size                              0.17874974
stargazer_count_normalized             0.07614058
fork_count_normalized                  0.09906453
Downloads_Normalized                   0.27854596
reverse_dep_normalized                 1.00000000

library(reshape2)
# Melt the correlation matrix
melted_correlation_matrix <- melt(correlation_matrix)

ggplot(data = melted_correlation_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = round(value, 2)), color = "black") +
  scale_fill_gradient2(low = "blue", high = "darkblue", mid = "lightblue", midpoint = 0, limit = c(-1, 1), space = "Lab", name="Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

3.4.5.2 non-normalized

# Calculate correlation between team_size and the normalized counts
correlation_matrix <- user_commits_distinct %>%
  select(team_size, stargazer_count, fork_count, Downloads_All_Time, Reverse_Depends_Count) %>%
  cor(use = "complete.obs")

# View the correlation matrix
print(correlation_matrix)

                      team_size stargazer_count fork_count Downloads_All_Time
team_size             1.0000000       0.7912326  0.7598055          0.3070778
stargazer_count       0.7912326       1.0000000  0.9419632          0.1674230
fork_count            0.7598055       0.9419632  1.0000000          0.1852880
Downloads_All_Time    0.3070778       0.1674230  0.1852880          1.0000000
Reverse_Depends_Count 0.2372756       0.1674554  0.1951223          0.4449235
                      Reverse_Depends_Count
team_size                         0.2372756
stargazer_count                   0.1674554
fork_count                        0.1951223
Downloads_All_Time                0.4449235
Reverse_Depends_Count             1.0000000

# Melt the correlation matrix
melted_correlation_matrix <- melt(correlation_matrix)

ggplot(data = melted_correlation_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = round(value, 2)), color = "black") +
  scale_fill_gradient2(low = "blue", high = "darkblue", mid = "lightblue", midpoint = 0, limit = c(-1, 1), space = "Lab", name="Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

3.4.6 correlation viz (no outliers z-score)

3.4.6.1 normalized

# Calculate correlation between team_size and the normalized counts
correlation_matrix <- user_commits_no_outliers_z %>%
  select(team_size, stargazer_count_normalized, fork_count_normalized, Downloads_Normalized, reverse_dep_normalized) %>%
  cor(use = "complete.obs")

# View the correlation matrix
print(correlation_matrix)

                           team_size stargazer_count_normalized
team_size                  1.0000000                 0.46840694
stargazer_count_normalized 0.4684069                 1.00000000
fork_count_normalized      0.5738767                 0.84916902
Downloads_Normalized       0.3410986                 0.13893791
reverse_dep_normalized     0.1162250                 0.04734392
                           fork_count_normalized Downloads_Normalized
team_size                              0.5738767            0.3410986
stargazer_count_normalized             0.8491690            0.1389379
fork_count_normalized                  1.0000000            0.1713354
Downloads_Normalized                   0.1713354            1.0000000
reverse_dep_normalized                 0.0756769            0.1463907
                           reverse_dep_normalized
team_size                              0.11622504
stargazer_count_normalized             0.04734392
fork_count_normalized                  0.07567690
Downloads_Normalized                   0.14639068
reverse_dep_normalized                 1.00000000

# Melt the correlation matrix
melted_correlation_matrix <- melt(correlation_matrix)

ggplot(data = melted_correlation_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = round(value, 2)), color = "black") +
  scale_fill_gradient2(low = "blue", high = "darkblue", mid = "lightblue", midpoint = 0, limit = c(-1, 1), space = "Lab", name="Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

3.4.6.2 non-normalized

# Calculate correlation between team_size and the normalized counts
correlation_matrix <- user_commits_no_outliers_z %>%
  select(team_size, stargazer_count, fork_count, Downloads_All_Time, Reverse_Depends_Count) %>%
  cor(use = "complete.obs")

# View the correlation matrix
print(correlation_matrix)

                      team_size stargazer_count fork_count Downloads_All_Time
team_size             1.0000000       0.5699283  0.6312434          0.3692510
stargazer_count       0.5699283       1.0000000  0.8223352          0.2075291
fork_count            0.6312434       0.8223352  1.0000000          0.2449997
Downloads_All_Time    0.3692510       0.2075291  0.2449997          1.0000000
Reverse_Depends_Count 0.1569053       0.1003055  0.1365102          0.2489343
                      Reverse_Depends_Count
team_size                         0.1569053
stargazer_count                   0.1003055
fork_count                        0.1365102
Downloads_All_Time                0.2489343
Reverse_Depends_Count             1.0000000

# Melt the correlation matrix
melted_correlation_matrix <- melt(correlation_matrix)

ggplot(data = melted_correlation_matrix, aes(x = Var1, y = Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = round(value, 2)), color = "black") +
  scale_fill_gradient2(low = "blue", high = "darkblue", mid = "lightblue", midpoint = 0, limit = c(-1, 1), space = "Lab", name="Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  labs(title = "Correlation Matrix Heatmap", x = "", y = "")

3.4.7 models (continuous team_size with outliers)

3.4.7.1 normalized

library(broom)

# Filter out non-positive values and missing values
user_commits_filtered1 <- user_commits_distinct %>%
  filter(stargazer_count_normalized > 0, !is.na(stargazer_count_normalized),
         fork_count_normalized > 0, !is.na(fork_count_normalized),
         Downloads_Normalized > 0, !is.na(Downloads_Normalized),
         reverse_dep_normalized > 0, !is.na(reverse_dep_normalized))

# Perform linear regression and get summary and confidence intervals
model_stargazer <- lm(stargazer_count_normalized ~ team_size, data = user_commits_filtered1)
model_fork <- lm(fork_count_normalized ~ team_size, data = user_commits_filtered1)
model_downloads <- lm(log(Downloads_Normalized) ~ team_size, data = user_commits_filtered1)
model_revdep <- lm(log(reverse_dep_normalized) ~ team_size, data = user_commits_filtered1)

summary(model_stargazer)

Call:
lm(formula = stargazer_count_normalized ~ team_size, data = user_commits_filtered1)

Residuals:
    Min      1Q  Median      3Q     Max 
-693.28   -3.34    3.33    7.63 1143.15 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -8.6722     3.5147  -2.467   0.0139 *  
team_size     2.3348     0.0823  28.368   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 73.48 on 503 degrees of freedom
Multiple R-squared:  0.6154,    Adjusted R-squared:  0.6146 
F-statistic: 804.8 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_stargazer)

                 2.5 %    97.5 %
(Intercept) -15.577516 -1.766800
team_size     2.173114  2.496518

summary(model_fork)

Call:
lm(formula = fork_count_normalized ~ team_size, data = user_commits_filtered1)

Residuals:
    Min      1Q  Median      3Q     Max 
-248.51   -0.83    2.98    4.34  411.02 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -5.16034    1.22170  -4.224 2.85e-05 ***
team_size    0.82062    0.02861  28.685  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 25.54 on 503 degrees of freedom
Multiple R-squared:  0.6206,    Adjusted R-squared:  0.6199 
F-statistic: 822.8 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_fork)

                 2.5 %     97.5 %
(Intercept) -7.5605956 -2.7600782
team_size    0.7644126  0.8768258

summary(model_downloads)

Call:
lm(formula = log(Downloads_Normalized) ~ team_size, data = user_commits_filtered1)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.4543 -1.7305 -0.1898  1.3366  5.3158 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 10.767840   0.097607 110.319   <2e-16 ***
team_size    0.021243   0.002286   9.294   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.041 on 503 degrees of freedom
Multiple R-squared:  0.1466,    Adjusted R-squared:  0.1449 
F-statistic: 86.38 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_downloads)

                  2.5 %      97.5 %
(Intercept) 10.57607342 10.95960736
team_size    0.01675265  0.02573381

summary(model_revdep)

Call:
lm(formula = log(reverse_dep_normalized) ~ team_size, data = user_commits_filtered1)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8156 -0.7941 -0.2377  0.5859  4.6569 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.304352   0.052235 -24.971  < 2e-16 ***
team_size    0.006325   0.001223   5.171 3.36e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.092 on 503 degrees of freedom
Multiple R-squared:  0.05048,   Adjusted R-squared:  0.04859 
F-statistic: 26.74 on 1 and 503 DF,  p-value: 3.364e-07

confint(model_revdep)

                   2.5 %       97.5 %
(Intercept) -1.406977950 -1.201726971
team_size    0.003922063  0.008728401

# Tidy the models
tidy_stargazer <- tidy(model_stargazer, conf.int = TRUE)
tidy_fork <- tidy(model_fork, conf.int = TRUE)
tidy_downloads <- tidy(model_downloads, conf.int = TRUE)
tidy_revdep <- tidy(model_revdep, conf.int = TRUE)

# Combine the tidied data
tidy_combined <- bind_rows(
  tidy_stargazer %>% mutate(model = "Stargazer Count Normalized"),
  tidy_fork %>% mutate(model = "Fork Count Normalized"),
  tidy_downloads %>% mutate(model = "Log Downloads Normalized"),
  tidy_revdep %>% mutate(model = "Log Rev Dep Normalized")
)

# Filter out the intercept terms
tidy_combined <- tidy_combined %>% filter(term == "team_size")

# Determine y-axis limits to ensure visibility of confidence intervals
y_limits <- range(tidy_combined$conf.low, tidy_combined$conf.high)

# Create the plot
ggplot(tidy_combined, aes(x = model, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray") +
  coord_flip() +
  scale_y_continuous(limits = y_limits) + # Adjust the limits based on confidence intervals
  labs(title = "Confidence Intervals for Team Size Coefficients",
       x = "Model",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5), # Reduce title size
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.position = "none"
  ) +
  scale_color_brewer(palette = "Set2")

3.4.7.2 non-normalized

# Filter out non-positive values and missing values
user_commits_filtered2 <- user_commits_distinct %>%
  filter(stargazer_count > 0, !is.na(stargazer_count),
         fork_count > 0, !is.na(fork_count),
         Downloads_All_Time > 0, !is.na(Downloads_All_Time),
         Reverse_Depends_Count > 0, !is.na(Reverse_Depends_Count))

# Perform linear regression and get summary and confidence intervals
model_stargazer <- lm(stargazer_count ~ team_size, data = user_commits_filtered2)
model_fork <- lm(fork_count ~ team_size, data = user_commits_filtered2)
model_downloads <- lm(log(Downloads_All_Time) ~ team_size, data = user_commits_filtered2)
model_revdep <- lm(log(Reverse_Depends_Count) ~ team_size, data = user_commits_filtered2)

summary(model_stargazer)

Call:
lm(formula = stargazer_count ~ team_size, data = user_commits_filtered2)

Residuals:
    Min      1Q  Median      3Q     Max 
-7359.0   -26.5    56.7    98.6 10930.6 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -119.8956    33.6007  -3.568 0.000394 ***
team_size     24.3104     0.7868  30.897  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 702.5 on 503 degrees of freedom
Multiple R-squared:  0.6549,    Adjusted R-squared:  0.6542 
F-statistic: 954.6 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_stargazer)

                 2.5 %    97.5 %
(Intercept) -185.91056 -53.88072
team_size     22.76449  25.85622

summary(model_fork)

Call:
lm(formula = fork_count ~ team_size, data = user_commits_filtered2)

Residuals:
    Min      1Q  Median      3Q     Max 
-2631.8    -9.0    33.7    46.1  3907.0 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -58.4527    12.1551  -4.809 2.01e-06 ***
team_size     8.5845     0.2846  30.160  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 254.1 on 503 degrees of freedom
Multiple R-squared:  0.6439,    Adjusted R-squared:  0.6432 
F-statistic: 909.6 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_fork)

                 2.5 %     97.5 %
(Intercept) -82.333718 -34.571606
team_size     8.025327   9.143766

summary(model_downloads)

Call:
lm(formula = log(Downloads_All_Time) ~ team_size, data = user_commits_filtered2)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.3728 -1.6455 -0.0803  1.5539  4.9093 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 12.920120   0.105600 122.350   <2e-16 ***
team_size    0.021367   0.002473   8.641   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.208 on 503 degrees of freedom
Multiple R-squared:  0.1293,    Adjusted R-squared:  0.1275 
F-statistic: 74.66 on 1 and 503 DF,  p-value: < 2.2e-16

confint(model_downloads)

                  2.5 %      97.5 %
(Intercept) 12.71264851 13.12759066
team_size    0.01650887  0.02622552

summary(model_revdep)

Call:
lm(formula = log(Reverse_Depends_Count) ~ team_size, data = user_commits_filtered2)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5184 -0.7519 -0.2850  0.5455  4.6885 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.727682   0.051652   14.09  < 2e-16 ***
team_size   0.008079   0.001210    6.68 6.37e-11 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.08 on 503 degrees of freedom
Multiple R-squared:  0.08147,   Adjusted R-squared:  0.07965 
F-statistic: 44.62 on 1 and 503 DF,  p-value: 6.366e-11

confint(model_revdep)

                  2.5 %     97.5 %
(Intercept) 0.626202254 0.82916095
team_size   0.005702663 0.01045532

# Tidy the models
tidy_stargazer <- tidy(model_stargazer, conf.int = TRUE)
tidy_fork <- tidy(model_fork, conf.int = TRUE)
tidy_downloads <- tidy(model_downloads, conf.int = TRUE)
tidy_revdep <- tidy(model_revdep, conf.int = TRUE)

# Combine the tidied data
tidy_combined <- bind_rows(
  tidy_stargazer %>% mutate(model = "Stargazer Count"),
  tidy_fork %>% mutate(model = "Fork Count"),
  tidy_downloads %>% mutate(model = "Downloads"),
  tidy_revdep %>% mutate(model = "Rev Dep")
)

# Filter out the intercept terms
tidy_combined <- tidy_combined %>% filter(term == "team_size")

# Determine y-axis limits to ensure visibility of confidence intervals
y_limits <- range(tidy_combined$conf.low, tidy_combined$conf.high)

# Create the plot
ggplot(tidy_combined, aes(x = model, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray") +
  coord_flip() +
  scale_y_continuous(limits = y_limits) + # Adjust the limits based on confidence intervals
  labs(title = "Confidence Intervals for Team Size Coefficients",
       x = "Model",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5), # Reduce title size
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.position = "none"
  ) +
  scale_color_brewer(palette = "Set2")

3.4.8 models (continuous team_size without outliers z-score)

3.4.8.1 normalized

# Filter out non-positive values and missing values
user_commits_filtered3 <- user_commits_no_outliers_z %>%
  filter(stargazer_count_normalized > 0, !is.na(stargazer_count_normalized),
         fork_count_normalized > 0, !is.na(fork_count_normalized),
         Downloads_Normalized > 0, !is.na(Downloads_Normalized),
         reverse_dep_normalized > 0, !is.na(reverse_dep_normalized))

# Perform linear regression and get summary and confidence intervals
model_stargazer <- lm(stargazer_count_normalized ~ team_size, data = user_commits_filtered3)
model_fork <- lm(fork_count_normalized ~ team_size, data = user_commits_filtered3)
model_downloads <- lm(log(Downloads_Normalized) ~ team_size, data = user_commits_filtered3)
model_revdep <- lm(log(reverse_dep_normalized) ~ team_size, data = user_commits_filtered3)

summary(model_stargazer)

Call:
lm(formula = stargazer_count_normalized ~ team_size, data = user_commits_filtered3)

Residuals:
    Min      1Q  Median      3Q     Max 
-67.293  -7.024  -2.675   0.630 290.788 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -0.9434     1.6179  -0.583     0.56    
team_size     2.0315     0.1269  16.005   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 26.34 on 475 degrees of freedom
Multiple R-squared:  0.3503,    Adjusted R-squared:  0.349 
F-statistic: 256.1 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_stargazer)

                2.5 %   97.5 %
(Intercept) -4.122506 2.235645
team_size    1.782113 2.280959

summary(model_fork)

Call:
lm(formula = fork_count_normalized ~ team_size, data = user_commits_filtered3)

Residuals:
    Min      1Q  Median      3Q     Max 
-12.757  -1.314  -0.325   0.376  35.070 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.30624    0.26143  -1.171    0.242    
team_size    0.44082    0.02051  21.492   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.257 on 475 degrees of freedom
Multiple R-squared:  0.493, Adjusted R-squared:  0.4919 
F-statistic: 461.9 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_fork)

                 2.5 %    97.5 %
(Intercept) -0.8199446 0.2074625
team_size    0.4005189 0.4811271

summary(model_downloads)

Call:
lm(formula = log(Downloads_Normalized) ~ team_size, data = user_commits_filtered3)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9299 -1.4529 -0.1037  1.2123  5.0561 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  9.93952    0.10758   92.39   <2e-16 ***
team_size    0.11191    0.00844   13.26   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.752 on 475 degrees of freedom
Multiple R-squared:  0.2701,    Adjusted R-squared:  0.2686 
F-statistic: 175.8 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_downloads)

                2.5 %     97.5 %
(Intercept) 9.7281316 10.1509103
team_size   0.0953228  0.1284931

summary(model_revdep)

Call:
lm(formula = log(reverse_dep_normalized) ~ team_size, data = user_commits_filtered3)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8315 -0.7179 -0.2284  0.5128  4.6767 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.505822   0.062169 -24.221  < 2e-16 ***
team_size    0.026514   0.004878   5.436 8.73e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.012 on 475 degrees of freedom
Multiple R-squared:  0.05856,   Adjusted R-squared:  0.05658 
F-statistic: 29.55 on 1 and 475 DF,  p-value: 8.734e-08

confint(model_revdep)

                  2.5 %      97.5 %
(Intercept) -1.62798240 -1.38366168
team_size    0.01692987  0.03609875

# Tidy the models
tidy_stargazer <- tidy(model_stargazer, conf.int = TRUE)
tidy_fork <- tidy(model_fork, conf.int = TRUE)
tidy_downloads <- tidy(model_downloads, conf.int = TRUE)
tidy_revdep <- tidy(model_revdep, conf.int = TRUE)

# Combine the tidied data
tidy_combined <- bind_rows(
  tidy_stargazer %>% mutate(model = "Stargazer Count Normalized"),
  tidy_fork %>% mutate(model = "Fork Count Normalized"),
  tidy_downloads %>% mutate(model = "Log Downloads Normalized"),
  tidy_revdep %>% mutate(model = "Log Rev Dep Normalized")
)

# Filter out the intercept terms
tidy_combined <- tidy_combined %>% filter(term == "team_size")

# Determine y-axis limits to ensure visibility of confidence intervals
y_limits <- range(tidy_combined$conf.low, tidy_combined$conf.high)

# Create the plot
ggplot(tidy_combined, aes(x = model, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray") +
  coord_flip() +
  scale_y_continuous(limits = y_limits) + # Adjust the limits based on confidence intervals
  labs(title = "Confidence Intervals for Team Size Coefficients",
       x = "Model",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5), # Reduce title size
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.position = "none"
  ) +
  scale_color_brewer(palette = "Set2")

3.4.8.2 non-normalized

# Filter out non-positive values and missing values
user_commits_filtered4 <- user_commits_no_outliers_z %>%
  filter(stargazer_count > 0, !is.na(stargazer_count),
         fork_count > 0, !is.na(fork_count),
         Downloads_All_Time > 0, !is.na(Downloads_All_Time),
         Reverse_Depends_Count > 0, !is.na(Reverse_Depends_Count))

# Perform linear regression and get summary and confidence intervals
model_stargazer <- lm(stargazer_count ~ team_size, data = user_commits_filtered4)
model_fork <- lm(fork_count ~ team_size, data = user_commits_filtered4)
model_downloads <- lm(log(Downloads_All_Time) ~ team_size, data = user_commits_filtered4)
model_revdep <- lm(log(Reverse_Depends_Count) ~ team_size, data = user_commits_filtered4)

summary(model_stargazer)

Call:
lm(formula = stargazer_count ~ team_size, data = user_commits_filtered4)

Residuals:
    Min      1Q  Median      3Q     Max 
-705.46  -57.03  -15.65    9.58 1986.20 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -20.870     13.223  -1.578    0.115    
team_size     18.762      1.037  18.085   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 215.3 on 475 degrees of freedom
Multiple R-squared:  0.4078,    Adjusted R-squared:  0.4065 
F-statistic: 327.1 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_stargazer)

                2.5 %    97.5 %
(Intercept) -46.85223  5.113025
team_size    16.72326 20.800338

summary(model_fork)

Call:
lm(formula = fork_count ~ team_size, data = user_commits_filtered4)

Residuals:
    Min      1Q  Median      3Q     Max 
-132.02  -11.93   -2.18    4.07  419.98 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -5.1981     2.4687  -2.106   0.0358 *  
team_size     4.1882     0.1937  21.624   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 40.2 on 475 degrees of freedom
Multiple R-squared:  0.4961,    Adjusted R-squared:  0.495 
F-statistic: 467.6 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_fork)

                 2.5 %    97.5 %
(Intercept) -10.048919 -0.347224
team_size     3.807587  4.568761

summary(model_downloads)

Call:
lm(formula = log(Downloads_All_Time) ~ team_size, data = user_commits_filtered4)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8558 -1.4676 -0.0868  1.3518  4.7035 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 12.082794   0.119156  101.40   <2e-16 ***
team_size    0.112912   0.009349   12.08   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.94 on 475 degrees of freedom
Multiple R-squared:  0.2349,    Adjusted R-squared:  0.2333 
F-statistic: 145.9 on 1 and 475 DF,  p-value: < 2.2e-16

confint(model_downloads)

                  2.5 %     97.5 %
(Intercept) 11.84865489 12.3169324
team_size    0.09454155  0.1312816

summary(model_revdep)

Call:
lm(formula = log(Reverse_Depends_Count) ~ team_size, data = user_commits_filtered4)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0066 -0.6009 -0.4928  0.4789  4.7078 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.456771   0.058992   7.743 5.91e-14 ***
team_size   0.036043   0.004628   7.787 4.33e-14 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9605 on 475 degrees of freedom
Multiple R-squared:  0.1132,    Adjusted R-squared:  0.1114 
F-statistic: 60.64 on 1 and 475 DF,  p-value: 4.326e-14

confint(model_revdep)

                 2.5 %    97.5 %
(Intercept) 0.34085461 0.5726880
team_size   0.02694846 0.0451376

# Tidy the models
tidy_stargazer <- tidy(model_stargazer, conf.int = TRUE)
tidy_fork <- tidy(model_fork, conf.int = TRUE)
tidy_downloads <- tidy(model_downloads, conf.int = TRUE)
tidy_revdep <- tidy(model_revdep, conf.int = TRUE)

# Combine the tidied data
tidy_combined <- bind_rows(
  tidy_stargazer %>% mutate(model = "Stargazer Count"),
  tidy_fork %>% mutate(model = "Fork Count"),
  tidy_downloads %>% mutate(model = "Downloads"),
  tidy_revdep %>% mutate(model = "Rev Dep")
)

# Filter out the intercept terms
tidy_combined <- tidy_combined %>% filter(term == "team_size")

# Determine y-axis limits to ensure visibility of confidence intervals
y_limits <- range(tidy_combined$conf.low, tidy_combined$conf.high)

# Create the plot
ggplot(tidy_combined, aes(x = model, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  geom_vline(xintercept = 0, linetype = "dashed", color = "gray") +
  coord_flip() +
  scale_y_continuous(limits = y_limits) + # Adjust the limits based on confidence intervals
  labs(title = "Confidence Intervals for Team Size Coefficients",
       x = "Model",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5), # Reduce title size
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.position = "none"
  ) +
  scale_color_brewer(palette = "Set2")

3.4.9 models (team_size_bin with outliers)

3.4.9.1 normalized

user_commits_filtered1$team_size_bin <- as.factor(user_commits_filtered1$team_size_bin)

# Perform regression analysis using team_size_bin
model_stargazer_bin <- lm(stargazer_count_normalized ~ team_size_bin, data = user_commits_filtered1)
model_fork_bin <- lm(fork_count_normalized ~ team_size_bin, data = user_commits_filtered1)
model_downloads_bin <- lm(Downloads_Normalized ~ team_size_bin, data = user_commits_filtered1)
model_revdep_bin <- lm(reverse_dep_normalized ~ team_size_bin, data = user_commits_filtered1)

# Tidy the models
tidy_stargazer_bin <- tidy(model_stargazer_bin, conf.int = TRUE)
tidy_fork_bin <- tidy(model_fork_bin, conf.int = TRUE)
tidy_downloads_bin <- tidy(model_downloads_bin, conf.int = TRUE)
tidy_revdep_bin <- tidy(model_revdep_bin, conf.int = TRUE)

# Combine the tidied data
tidy_combined_bin <- bind_rows(
  tidy_stargazer_bin %>% mutate(model = "Stargazer Count Normalized"),
  tidy_fork_bin %>% mutate(model = "Fork Count Normalized"),
  tidy_downloads_bin %>% mutate(model = "Downloads Normalized"),
  tidy_revdep_bin %>% mutate(model = "Rev Dep Normalized")
)

# Filter out the intercept terms
tidy_combined_bin <- tidy_combined_bin %>% filter(term != "(Intercept)")

# Create the plot
ggplot(tidy_combined_bin, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  coord_flip() +
  facet_wrap(~ model, scales = "free_y") +
  labs(title = "Regression Coefficients for Team Size Bins",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = "bold"),
    legend.position = "bottom",
    legend.title = element_blank()
  ) +
  scale_color_brewer(palette = "Set1")

ggplot(tidy_stargazer_bin %>% filter(term != "(Intercept)"), 
       aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(size = 1.2, color = "darkblue") +
  geom_point(size = 4, shape = 21, fill = "blue") +
  coord_flip() +
  labs(title = "Regression Coefficients for Team Size Bins (Stargazer Count Normalized)",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_bw() +
  theme(
    plot.title = element_text(size = 10, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    panel.grid.major = element_line(color = "gray", size = 0.5),
    panel.grid.minor = element_line(color = "lightgray", size = 0.25),
    panel.background = element_rect(fill = "white", color = "black")
  )

ggplot(tidy_fork_bin %>% filter(term != "(Intercept)"), 
       aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(size = 1.2, color = "darkgreen") +
  geom_point(size = 4, shape = 21, fill = "green") +
  coord_flip() +
  labs(title = "Regression Coefficients for Team Size Bins (Fork Count Normalized)",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_bw() +
  theme(
    plot.title = element_text(size = 10, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    panel.grid.major = element_line(color = "gray", size = 0.5),
    panel.grid.minor = element_line(color = "lightgray", size = 0.25),
    panel.background = element_rect(fill = "white", color = "black")
  )

ggplot(tidy_downloads_bin %>% filter(term != "(Intercept)"), 
       aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(size = 1.2, color = "darkred") +
  geom_point(size = 4, shape = 21, fill = "red") +
  coord_flip() +
  labs(title = "Regression Coefficients for Team Size Bins (Log Downloads Normalized)",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_bw() +
  theme(
    plot.title = element_text(size = 10, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    panel.grid.major = element_line(color = "gray", size = 0.5),
    panel.grid.minor = element_line(color = "lightgray", size = 0.25),
    panel.background = element_rect(fill = "white", color = "black")
  )

ggplot(tidy_revdep_bin %>% filter(term != "(Intercept)"), 
       aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
  geom_pointrange(size = 1.2, color = "darkred") +
  geom_point(size = 4, shape = 21, fill = "red") +
  coord_flip() +
  labs(title = "Regression Coefficients for Team Size Bins (Log revdep Normalized)",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_bw() +
  theme(
    plot.title = element_text(size = 10, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 14, face = "bold"),
    axis.title.y = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    panel.grid.major = element_line(color = "gray", size = 0.5),
    panel.grid.minor = element_line(color = "lightgray", size = 0.25),
    panel.background = element_rect(fill = "white", color = "black")
  )

library(multcomp)

# Perform Tukey's HSD test
tukey_test <- glht(model_stargazer_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = stargazer_count_normalized ~ team_size_bin, data = user_commits_filtered1)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0          2.077     21.749   0.095  0.99998    
[3-4] - [1] == 0        3.696     20.232   0.183  0.99974    
[5-7] - [1] == 0        7.903     21.749   0.363  0.99613    
[8-880] - [1] == 0     62.245     19.093   3.260  0.01008 *  
[3-4] - [2] == 0        1.620     17.061   0.095  0.99998    
[5-7] - [2] == 0        5.826     18.835   0.309  0.99793    
[8-880] - [2] == 0     60.168     15.694   3.834  0.00133 ** 
[5-7] - [3-4] == 0      4.206     17.061   0.247  0.99915    
[8-880] - [3-4] == 0   58.548     13.514   4.332  < 0.001 ***
[8-880] - [5-7] == 0   54.342     15.694   3.463  0.00499 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_fork_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = fork_count_normalized ~ team_size_bin, data = user_commits_filtered1)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)   
[2] - [1] == 0         0.2323     7.6877   0.030  1.00000   
[3-4] - [1] == 0       0.7922     7.1516   0.111  0.99996   
[5-7] - [1] == 0       1.8214     7.6877   0.237  0.99927   
[8-880] - [1] == 0    17.6007     6.7491   2.608  0.06775 . 
[3-4] - [2] == 0       0.5599     6.0308   0.093  0.99998   
[5-7] - [2] == 0       1.5891     6.6578   0.239  0.99925   
[8-880] - [2] == 0    17.3684     5.5476   3.131  0.01517 * 
[5-7] - [3-4] == 0     1.0292     6.0308   0.171  0.99980   
[8-880] - [3-4] == 0  16.8085     4.7770   3.519  0.00411 **
[8-880] - [5-7] == 0  15.7793     5.5476   2.844  0.03582 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_revdep_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = reverse_dep_normalized ~ team_size_bin, data = user_commits_filtered1)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)  
[2] - [1] == 0       -0.18017    0.53574  -0.336   0.9971  
[3-4] - [1] == 0     -0.05556    0.49838  -0.111   1.0000  
[5-7] - [1] == 0      0.77634    0.53574   1.449   0.5881  
[8-880] - [1] == 0    1.00912    0.47033   2.146   0.1968  
[3-4] - [2] == 0      0.12462    0.42027   0.297   0.9982  
[5-7] - [2] == 0      0.95651    0.46396   2.062   0.2320  
[8-880] - [2] == 0    1.18929    0.38660   3.076   0.0181 *
[5-7] - [3-4] == 0    0.83189    0.42027   1.979   0.2705  
[8-880] - [3-4] == 0  1.06468    0.33290   3.198   0.0122 *
[8-880] - [5-7] == 0  0.23278    0.38660   0.602   0.9738  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_downloads_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Downloads_Normalized ~ team_size_bin, data = user_commits_filtered1)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0          16770     310663   0.054    1.000    
[3-4] - [1] == 0       -19646     288997  -0.068    1.000    
[5-7] - [1] == 0       110083     310663   0.354    0.996    
[8-880] - [1] == 0    1501247     272734   5.504   <1e-05 ***
[3-4] - [2] == 0       -36416     243704  -0.149    1.000    
[5-7] - [2] == 0        93313     269042   0.347    0.997    
[8-880] - [2] == 0    1484477     224178   6.622   <1e-05 ***
[5-7] - [3-4] == 0     129729     243704   0.532    0.983    
[8-880] - [3-4] == 0  1520893     193039   7.879   <1e-05 ***
[8-880] - [5-7] == 0  1391164     224178   6.206   <1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

3.4.9.2 nonnormalzied

user_commits_filtered2$team_size_bin <- as.factor(user_commits_filtered2$team_size_bin)

# Perform regression analysis using team_size_bin
model_stargazer_bin <- lm(stargazer_count ~ team_size_bin, data = user_commits_filtered2)
model_fork_bin <- lm(fork_count ~ team_size_bin, data = user_commits_filtered2)
model_downloads_bin <- lm(Downloads_All_Time ~ team_size_bin, data = user_commits_filtered2)
model_revdep_bin <- lm(Reverse_Depends_Count ~ team_size_bin, data = user_commits_filtered2)

# Tidy the models
tidy_stargazer_bin <- tidy(model_stargazer_bin, conf.int = TRUE)
tidy_fork_bin <- tidy(model_fork_bin, conf.int = TRUE)
tidy_downloads_bin <- tidy(model_downloads_bin, conf.int = TRUE)
tidy_revdep_bin <- tidy(model_revdep_bin, conf.int = TRUE)

# Combine the tidied data
tidy_combined_bin <- bind_rows(
  tidy_stargazer_bin %>% mutate(model = "Stargazer Count"),
  tidy_fork_bin %>% mutate(model = "Fork Count"),
  tidy_downloads_bin %>% mutate(model = "Downloads"),
  tidy_revdep_bin %>% mutate(model = "Rev Dep")
)

# Filter out the intercept terms
tidy_combined_bin <- tidy_combined_bin %>% filter(term != "(Intercept)")

# Create the plot
ggplot(tidy_combined_bin, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  coord_flip() +
  facet_wrap(~ model, scales = "free_y") +
  labs(title = "Regression Coefficients for Team Size Bins",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = "bold"),
    legend.position = "bottom",
    legend.title = element_blank()
  ) +
  scale_color_brewer(palette = "Set1")

# Perform Tukey's HSD test
tukey_test <- glht(model_stargazer_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = stargazer_count ~ team_size_bin, data = user_commits_filtered2)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0          17.24     219.92   0.078  0.99999    
[3-4] - [1] == 0        29.94     204.58   0.146  0.99989    
[5-7] - [1] == 0        64.85     219.92   0.295  0.99828    
[8-880] - [1] == 0     603.08     193.07   3.124  0.01544 *  
[3-4] - [2] == 0        12.71     172.52   0.074  0.99999    
[5-7] - [2] == 0        47.61     190.46   0.250  0.99910    
[8-880] - [2] == 0     585.85     158.70   3.692  0.00221 ** 
[5-7] - [3-4] == 0      34.91     172.52   0.202  0.99961    
[8-880] - [3-4] == 0   573.14     136.65   4.194  < 0.001 ***
[8-880] - [5-7] == 0   538.23     158.70   3.392  0.00642 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_fork_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = fork_count ~ team_size_bin, data = user_commits_filtered2)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)   
[2] - [1] == 0          2.280     78.960   0.029  1.00000   
[3-4] - [1] == 0        7.289     73.453   0.099  0.99998   
[5-7] - [1] == 0       16.253     78.960   0.206  0.99958   
[8-880] - [1] == 0    179.502     69.320   2.589  0.07113 . 
[3-4] - [2] == 0        5.009     61.941   0.081  0.99999   
[5-7] - [2] == 0       13.973     68.381   0.204  0.99960   
[8-880] - [2] == 0    177.222     56.979   3.110  0.01614 * 
[5-7] - [3-4] == 0      8.964     61.941   0.145  0.99990   
[8-880] - [3-4] == 0  172.213     49.064   3.510  0.00428 **
[8-880] - [5-7] == 0  163.249     56.979   2.865  0.03379 * 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_revdep_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Reverse_Depends_Count ~ team_size_bin, data = user_commits_filtered2)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)   
[2] - [1] == 0        -0.4311     5.5480  -0.078  0.99999   
[3-4] - [1] == 0       0.4615     5.1611   0.089  0.99998   
[5-7] - [1] == 0       4.2622     5.5480   0.768  0.93756   
[8-880] - [1] == 0    12.5728     4.8707   2.581  0.07263 . 
[3-4] - [2] == 0       0.8926     4.3522   0.205  0.99959   
[5-7] - [2] == 0       4.6933     4.8047   0.977  0.86161   
[8-880] - [2] == 0    13.0039     4.0035   3.248  0.01043 * 
[5-7] - [3-4] == 0     3.8007     4.3522   0.873  0.90365   
[8-880] - [3-4] == 0  12.1113     3.4474   3.513  0.00417 **
[8-880] - [5-7] == 0   8.3106     4.0035   2.076  0.22580   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_downloads_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Downloads_All_Time ~ team_size_bin, data = user_commits_filtered2)

Linear Hypotheses:
                     Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0          98092    2685882   0.037    1.000    
[3-4] - [1] == 0      -201122    2498570  -0.080    1.000    
[5-7] - [1] == 0      1193374    2685882   0.444    0.992    
[8-880] - [1] == 0   13768280    2357961   5.839   <1e-05 ***
[3-4] - [2] == 0      -299214    2106979  -0.142    1.000    
[5-7] - [2] == 0      1095282    2326042   0.471    0.990    
[8-880] - [2] == 0   13670188    1938167   7.053   <1e-05 ***
[5-7] - [3-4] == 0    1394496    2106979   0.662    0.963    
[8-880] - [3-4] == 0 13969402    1668946   8.370   <1e-05 ***
[8-880] - [5-7] == 0 12574906    1938167   6.488   <1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

3.4.10 models (team_size_bin with no outliers z-score)

3.4.10.1 normalized

user_commits_filtered3$team_size_bin <- as.factor(user_commits_filtered3$team_size_bin)

# Perform regression analysis using team_size_bin
model_stargazer_bin <- lm(stargazer_count_normalized ~ team_size_bin, data = user_commits_filtered3)
model_fork_bin <- lm(fork_count_normalized ~ team_size_bin, data = user_commits_filtered3)
model_downloads_bin <- lm(Downloads_Normalized ~ team_size_bin, data = user_commits_filtered3)
model_revdep_bin <- lm(reverse_dep_normalized ~ team_size_bin, data = user_commits_filtered3)

# Tidy the models
tidy_stargazer_bin <- tidy(model_stargazer_bin, conf.int = TRUE)
tidy_fork_bin <- tidy(model_fork_bin, conf.int = TRUE)
tidy_downloads_bin <- tidy(model_downloads_bin, conf.int = TRUE)
tidy_revdep_bin <- tidy(model_revdep_bin, conf.int = TRUE)

# Combine the tidied data
tidy_combined_bin <- bind_rows(
  tidy_stargazer_bin %>% mutate(model = "Stargazer Count Normalized"),
  tidy_fork_bin %>% mutate(model = "Fork Count Normalized"),
  tidy_downloads_bin %>% mutate(model = "Downloads Normalized"),
  tidy_revdep_bin %>% mutate(model = "Rev Dep Normalized")
)

# Filter out the intercept terms
tidy_combined_bin <- tidy_combined_bin %>% filter(term != "(Intercept)")

# Create the plot
ggplot(tidy_combined_bin, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  coord_flip() +
  facet_wrap(~ model, scales = "free_y") +
  labs(title = "Regression Coefficients for Team Size Bins",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = "bold"),
    legend.position = "bottom",
    legend.title = element_blank()
  ) +
  scale_color_brewer(palette = "Set1")

# Perform Tukey's HSD test
tukey_test <- glht(model_stargazer_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = stargazer_count_normalized ~ team_size_bin, data = user_commits_filtered3)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0         2.077      5.494   0.378    0.996    
[3-4] - [1] == 0       3.696      5.111   0.723    0.950    
[5-6] - [1] == 0       7.903      5.494   1.438    0.596    
[7-58] - [1] == 0     34.909      4.900   7.124   <1e-04 ***
[3-4] - [2] == 0       1.620      4.310   0.376    0.996    
[5-6] - [2] == 0       5.826      4.758   1.225    0.732    
[7-58] - [2] == 0     32.833      4.058   8.092   <1e-04 ***
[5-6] - [3-4] == 0     4.206      4.310   0.976    0.863    
[7-58] - [3-4] == 0   31.213      3.521   8.864   <1e-04 ***
[7-58] - [5-6] == 0   27.006      4.058   6.656   <1e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_fork_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = fork_count_normalized ~ team_size_bin, data = user_commits_filtered3)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0        0.2323     0.9737   0.239    0.999    
[3-4] - [1] == 0      0.7922     0.9058   0.875    0.904    
[5-6] - [1] == 0      1.8214     0.9737   1.871    0.328    
[7-58] - [1] == 0     7.0720     0.8685   8.143   <1e-04 ***
[3-4] - [2] == 0      0.5599     0.7639   0.733    0.947    
[5-6] - [2] == 0      1.5891     0.8433   1.884    0.321    
[7-58] - [2] == 0     6.8397     0.7192   9.511   <1e-04 ***
[5-6] - [3-4] == 0    1.0292     0.7639   1.347    0.655    
[7-58] - [3-4] == 0   6.2798     0.6241  10.062   <1e-04 ***
[7-58] - [5-6] == 0   5.2506     0.7192   7.301   <1e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_revdep_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = reverse_dep_normalized ~ team_size_bin, data = user_commits_filtered3)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)  
[2] - [1] == 0      -0.18017    0.45490  -0.396   0.9946  
[3-4] - [1] == 0    -0.05556    0.42317  -0.131   0.9999  
[5-6] - [1] == 0     0.77634    0.45490   1.707   0.4239  
[7-58] - [1] == 0    0.65605    0.40571   1.617   0.4801  
[3-4] - [2] == 0     0.12462    0.35685   0.349   0.9967  
[5-6] - [2] == 0     0.95651    0.39395   2.428   0.1066  
[7-58] - [2] == 0    0.83623    0.33596   2.489   0.0922 .
[5-6] - [3-4] == 0   0.83189    0.35685   2.331   0.1331  
[7-58] - [3-4] == 0  0.71161    0.29157   2.441   0.1033  
[7-58] - [5-6] == 0 -0.12028    0.33596  -0.358   0.9964  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_downloads_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Downloads_Normalized ~ team_size_bin, data = user_commits_filtered3)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0         16770     245757   0.068    1.000    
[3-4] - [1] == 0      -19646     228618  -0.086    1.000    
[5-6] - [1] == 0      110083     245757   0.448    0.991    
[7-58] - [1] == 0    1035129     219187   4.723 3.01e-05 ***
[3-4] - [2] == 0      -36416     192788  -0.189    1.000    
[5-6] - [2] == 0       93313     212832   0.438    0.992    
[7-58] - [2] == 0    1018359     181504   5.611  < 1e-05 ***
[5-6] - [3-4] == 0    129729     192788   0.673    0.961    
[7-58] - [3-4] == 0  1054775     157522   6.696  < 1e-05 ***
[7-58] - [5-6] == 0   925047     181504   5.097  < 1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

3.4.10.2 nonnormalzied

user_commits_filtered4$team_size_bin <- as.factor(user_commits_filtered4$team_size_bin)

# Perform regression analysis using team_size_bin
model_stargazer_bin <- lm(stargazer_count ~ team_size_bin, data = user_commits_filtered4)
model_fork_bin <- lm(fork_count ~ team_size_bin, data = user_commits_filtered4)
model_downloads_bin <- lm(Downloads_All_Time ~ team_size_bin, data = user_commits_filtered4)
model_revdep_bin <- lm(Reverse_Depends_Count ~ team_size_bin, data = user_commits_filtered4)

# Tidy the models
tidy_stargazer_bin <- tidy(model_stargazer_bin, conf.int = TRUE)
tidy_fork_bin <- tidy(model_fork_bin, conf.int = TRUE)
tidy_downloads_bin <- tidy(model_downloads_bin, conf.int = TRUE)
tidy_revdep_bin <- tidy(model_revdep_bin, conf.int = TRUE)

# Combine the tidied data
tidy_combined_bin <- bind_rows(
  tidy_stargazer_bin %>% mutate(model = "Stargazer Count"),
  tidy_fork_bin %>% mutate(model = "Fork Count"),
  tidy_downloads_bin %>% mutate(model = "Downloads"),
  tidy_revdep_bin %>% mutate(model = "Rev Dep")
)

# Filter out the intercept terms
tidy_combined_bin <- tidy_combined_bin %>% filter(term != "(Intercept)")

# Create the plot
ggplot(tidy_combined_bin, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high, color = model)) +
  geom_pointrange(size = 1.2) +
  geom_point(size = 3) +
  coord_flip() +
  facet_wrap(~ model, scales = "free_y") +
  labs(title = "Regression Coefficients for Team Size Bins",
       x = "Team Size Bin",
       y = "Coefficient Estimate") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = "bold"),
    legend.position = "bottom",
    legend.title = element_blank()
  ) +
  scale_color_brewer(palette = "Set1")

# Perform Tukey's HSD test
tukey_test <- glht(model_stargazer_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = stargazer_count ~ team_size_bin, data = user_commits_filtered4)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0         17.24      46.46   0.371    0.996    
[3-4] - [1] == 0       29.94      43.22   0.693    0.957    
[5-6] - [1] == 0       64.85      46.46   1.396    0.624    
[7-58] - [1] == 0     309.56      41.44   7.470   <1e-04 ***
[3-4] - [2] == 0       12.71      36.45   0.349    0.997    
[5-6] - [2] == 0       47.61      40.24   1.183    0.756    
[7-58] - [2] == 0     292.32      34.32   8.519   <1e-04 ***
[5-6] - [3-4] == 0     34.91      36.45   0.958    0.871    
[7-58] - [3-4] == 0   279.61      29.78   9.389   <1e-04 ***
[7-58] - [5-6] == 0   244.71      34.32   7.131   <1e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_fork_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = fork_count ~ team_size_bin, data = user_commits_filtered4)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0         2.280      9.277   0.246    0.999    
[3-4] - [1] == 0       7.289      8.630   0.845    0.914    
[5-6] - [1] == 0      16.253      9.277   1.752    0.396    
[7-58] - [1] == 0     65.752      8.274   7.947   <1e-04 ***
[3-4] - [2] == 0       5.009      7.278   0.688    0.958    
[5-6] - [2] == 0      13.973      8.034   1.739    0.404    
[7-58] - [2] == 0     63.472      6.852   9.264   <1e-04 ***
[5-6] - [3-4] == 0     8.964      7.278   1.232    0.727    
[7-58] - [3-4] == 0   58.463      5.946   9.832   <1e-04 ***
[7-58] - [5-6] == 0   49.498      6.852   7.224   <1e-04 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_revdep_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Reverse_Depends_Count ~ team_size_bin, data = user_commits_filtered4)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)  
[2] - [1] == 0       -0.4311     3.4936  -0.123   0.9999  
[3-4] - [1] == 0      0.4615     3.2499   0.142   0.9999  
[5-6] - [1] == 0      4.2622     3.4936   1.220   0.7344  
[7-58] - [1] == 0     7.6828     3.1158   2.466   0.0974 .
[3-4] - [2] == 0      0.8926     2.7406   0.326   0.9975  
[5-6] - [2] == 0      4.6933     3.0255   1.551   0.5226  
[7-58] - [2] == 0     8.1139     2.5802   3.145   0.0147 *
[5-6] - [3-4] == 0    3.8007     2.7406   1.387   0.6300  
[7-58] - [3-4] == 0   7.2213     2.2393   3.225   0.0115 *
[7-58] - [5-6] == 0   3.4206     2.5802   1.326   0.6692  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)

# Perform Tukey's HSD test
tukey_test <- glht(model_downloads_bin, linfct = mcp(team_size_bin = "Tukey"))

# Summary of the Tukey test results
summary(tukey_test)

     Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lm(formula = Downloads_All_Time ~ team_size_bin, data = user_commits_filtered4)

Linear Hypotheses:
                    Estimate Std. Error t value Pr(>|t|)    
[2] - [1] == 0         98092    2054842   0.048    1.000    
[3-4] - [1] == 0     -201122    1911539  -0.105    1.000    
[5-6] - [1] == 0     1193374    2054842   0.581    0.977    
[7-58] - [1] == 0    9449024    1832678   5.156   <1e-05 ***
[3-4] - [2] == 0     -299214    1611951  -0.186    1.000    
[5-6] - [2] == 0     1095282    1779546   0.615    0.972    
[7-58] - [2] == 0    9350931    1517602   6.162   <1e-05 ***
[5-6] - [3-4] == 0   1394496    1611951   0.865    0.907    
[7-58] - [3-4] == 0  9650145    1317087   7.327   <1e-05 ***
[7-58] - [5-6] == 0  8255650    1517602   5.440   <1e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)